Estimating the true prevalence of an epidemic outbreak is a key public health problem. This is challenging because surveillance is usually resource intensive and biased. In the network setting, prior work on cost sensitive disease surveillance has focused on choosing a subset of individuals (or nodes) to minimize objectives such as probability of outbreak detection. Such methods do not give insights into the outbreak size distribution which, despite being complex and multi-modal, is very useful in public health planning. We introduce TESTPREV, a problem of choosing a subset of nodes which maximizes the mutual information with disease prevalence, which directly provides information about the outbreak size distribution. We show that, under the independent cascade (IC) model, solutions computed by all prior disease surveillance approaches are highly sub-optimal for TESTPREV in general. We also show that TESTPREV is hard to even approximate. While this mutual information objective is computationally challenging for general networks, we show that it can be computed efficiently for various network classes. We present a greedy strategy, called GREEDYMI, that uses estimates of mutual information from cascade simulations and thus can be applied on any network and disease model. We find that GREEDYMI does better than natural baselines in terms of maximizing the mutual information as well as reducing the expected variance in outbreak size, under the IC model.

As AI algorithms are deployed extensively, the need to ensure the fairness of their outputs is critical. Most existing work is on “fairness by design” approaches that incorporate limited tests for fairness into a limited number of algorithms. Here, we explore a framework that removes these limitations and can be used with any algorithm’s output that allocates instances to one of K categories/classes such as outlier detection (OD), clustering and classification. The framework can encode standard and novel fairness types beyond simple counting, and importantly, it can detect intersectional unfairness without being specifically told what to look for. Our experimental results show that both standard and novel types of unfairness exist extensively in the outputs of fair-by-design algorithms and the counter-intuitive result that they can actually increase intersectional unfairness.

We consider a setting where goods are allocated to agents by way of an allocation platform (e.g., a matching platform). An “allocation facilitator” aims to increase the overall utility/social-good of the allocation by encouraging (some of the) agents to relax (some of) their restrictions. At the same time, the advice must not hurt agents who would otherwise be better off. Additionally, the facilitator may be constrained by a “bound” (a.k.a. ‘budget’), limiting the number and/or type of restrictions it may seek to relax. We consider the facilitator’s optimization problem of choosing an optimal set of restrictions to request to relax under the aforementioned constraints. Our contributions are three-fold: (i) We provide a formal definition of the problem, including the participation guarantees to which the facilitator should adhere. We define a hierarchy of participation guarantees and also consider several social-good functions. (ii) We provide polynomial algorithms for solving various versions of the associated optimization problems, including one-to-one and many-to-one allocation settings. (iii) We demonstrate the benefits of such facilitation and relaxation, and the implications of the different participation guarantees, using extensive experimentation on three real-world datasets.

Accurate canal network mapping is essential for water management, including irrigation planning and infrastructure maintenance. State-of-the-art semantic segmentation models for infrastructure mapping, such as roads, rely on large, well-annotated remote sensing datasets. However, incomplete or inadequate ground truth can hinder these learning approaches. Many infrastructure networks have graph-level properties such as reachability to a source (like canals) or connectivity (roads) that can be leveraged to improve these existing ground truth. This paper develops a novel iterative framework IGraSS, combining a semantic segmentation module—incorporating RGB and additional modalities (NDWI, DEM)—with a graph-based ground-truth refinement module. The segmentation module processes satellite imagery patches, while the refinement module operates on the entire data viewing the infrastructure network as a graph. Experiments show that IGraSS reduces unreachable canal segments from ~18% to ~3%, and training with refined ground truth significantly improves canal identification. IGraSS serves as a robust framework for both refining noisy ground truth and mapping canal networks from remote sensing imagery. We also demonstrate the effectiveness and generalizability of IGraSS using road networks as an example, applying a different graph-theoretic constraint to complete road networks.

The Russian invasion of Ukraine in February 2022 has led to the largest forced migration crisis in Europe since World War II, with millions displaced both internally and internationally. Among the displaced, approximately 4.2 million individuals have returned, highlighting the significance of return migration as a critical phase in the migration continuum. Existing studies on return migration are limited in scope, relying on survey-based approaches that suffer from demographic bias, lack of validation against ground truth, and inability to account for uncertainty. We propose a novel computational framework for modeling the return of conflict-induced migrants, using agent-based models (ABMs) and their surrogates. These models are grounded in hazard functions and account for sociopolitical contexts. Our proposed ABMs outperform baseline methods in estimating return migration from Poland to Ukraine by at least 42% and by as much as 57% in terms of normalized root mean squared error (NRMSE). Further, to illustrate the utility of such models for policymakers, we conduct two case studies that estimate the duration of displacement and characterize the demographic breakdown among the returnees.

Discrete dynamical systems are commonly used to model the spread of contagions on real-world networks. Under the PAC framework, existing research has studied the problem of learning the behavior of a system, assuming that the underlying network is known. In this work, we focus on a more challenging setting: to learn both the behavior and the underlying topology of a black-box system. We show that, in general, this learning problem is computationally intractable. On the positive side, we present efficient learning methods under the PAC model when the underlying graph of the dynamical system belongs to certain classes. Further, we examine a relaxed setting where the topology of an unknown system is partially observed. For this case, we develop an efficient PAC learner to infer the system and establish the sample complexity. Lastly, we present a formal analysis of the expressive power of the hypothesis class of dynamical systems where both the topology and behavior are unknown, using the well-known Natarajan dimension formalism. Our results provide a theoretical foundation for learning both the topology and behavior of discrete dynamical systems.

Networked dynamical systems are widely used as formal models of real-world cascading phenomena, such as the spread of diseases and information. Prior research has addressed the problem of learning the behavior of an unknown dynamical system when the underlying network has a single layer. In this work, we study the learnability of dynamical systems over multilayer networks, which are more realistic and challenging. First, we present an efficient PAC learning algorithm with provable guarantees to show that the learner only requires a small number of training examples to infer an unknown system. We further provide a tight analysis of the Natarajan dimension which measures the model complexity. Asymptotically, our bound on the Nararajan dimension is tight for almost all multilayer graphs. The techniques and insights from our work provide the theoretical foundations for future investigations of learning problems for multilayer dynamical systems.

Explainable AI (XAI) is an important area but remains relatively understudied for clustering. We propose an explainable-by-design clustering approach that not only finds clusters but also exemplars to explain each cluster. The use of exemplars for understanding is supported by the exemplar-based school of concept definition in psychology. We show that finding a small set of exemplars to explain even a single cluster is computationally intractable; hence, the overall problem is challenging. We develop an approximation algorithm that provides provable performance guarantees with respect to clustering quality as well as the number of exemplars used. This basic algorithm explains all the instances in every cluster whilst another approximation algorithm uses a bounded number of exemplars to allow simpler explanations and provably covers a large fraction of all the instances. Experimental results show that our work is useful in domains involving difficult to understand deep embeddings of images and text.

Evolutionary anti-coordination games on networks capture real-world strategic situations such as traffic routing and market competition. Two key problems concerning evolutionary games are the existence of a pure Nash equilibrium (NE) and the convergence time. In this work, we study these two problems for anti-coordination games under sequential and synchronous update schemes. For each update scheme, we examine two decision modes based on whether an agent considers its own previous action (self essential) or not (self non-essential) in choosing its next action. Using a relationship between games and dynamical systems, we show that for both update schemes, finding an NE can be done efficiently under the self non-essential mode but is computationally intractable under the self essential mode. We then identify special cases for which an NE can be obtained efficiently. For convergence time, we show that the dynamics converges in a polynomial number of steps under the synchronous scheme; for the sequential scheme, the convergence time is polynomial only under the self non-essential mode. Through experiments, we empirically examine the convergence time and the equilibria for both synthetic and real-world networks.

Applications such as employees sharing office spaces over a workweek can be modeled as problems where agents are matched to resources over multiple rounds. Agents' requirements limit the set of compatible resources and the rounds in which they want to be matched. Viewing such an application as a multi-round matching problem on a bipartite compatibility graph between agents and resources, we show that a solution (i.e., a set of matchings, with one matching per round) can be found efficiently if one exists. To cope with situations where a solution does not exist, we consider two extensions. In the first extension, a benefit function is defined for each agent and the objective is to find a multi-round matching to maximize the total benefit. For a general class of benefit functions satisfying certain properties (including diminishing returns), we show that this multi-round matching problem is efficiently solvable. This class includes utilitarian and Rawlsian welfare functions. For another benefit function, we show that the maximization problem is NP-hard. In the second extension, the objective is to generate advice to each agent (i.e., a subset of requirements to be relaxed) subject to a budget constraint so that the agent can be matched. We show that this budget-constrained advice generation problem is NP-hard. For this problem, we develop an integer linear programming formulation as well as a heuristic based on local search. We experimentally evaluate our algorithms on synthetic networks and apply them to two real-world situations: shared office spaces and matching courses to classrooms.

Transitioning to clean and low-carbon energy is becoming a crucial goal for many entities in the energy systems sector such as governments, power utilities, and policymakers. This shift to clean energy is supported by a diverse portfolio of data products such as satellite data, smart meter data, power networks, green energy datasets (e.g., solar installations & electric vehicles), microgrid networks, and building stock data. Among these, network datasets are becoming increasingly common in addressing a wide array of issues in residential energy, especially in applications that focus on social good. Thus, streamlining the process of generating different types of networks will be helpful. In this work, we propose a versatile network synthesis and analytics pipeline developed using software design principles that make it modular, scalable, and extensible. Three case studies are presented to illustrate the significance of network data in sustainable energy applications.

We consider the setting of cascades that result from contagion dynamics on large realistic contact networks. We address the question of whether the structural properties of a (partially) observed cascade can characterize the contagion scenario and identify the interventions that might be in effect. Using epidemic spread as a concrete example, we study how social interventions such as compliance in social distancing, extent (and efficacy) of vaccination, and the transmissibility of disease can be inferred. The techniques developed are more generally applicable to other contagions as well.

Networked discrete dynamical systems are often used to model the spread of contagions and decision-making by agents in coordination games. Fixed points of such dynamical systems represent configurations to which the system converges. In the dissemination of undesirable contagions (such as rumors and misinformation), convergence to fixed points with a small number of affected nodes is a desirable goal. Motivated by such considerations, we formulate a novel optimization problem of finding a nontrivial fixed point of the system with the minimum number of affected nodes. We establish that, unless P = NP, there is no polynomial-time algorithm for approximating a solution to this problem to within the factor n^(1 - epsilon) for any constant epsilon > 0. To cope with this computational intractability, we identify several special cases for which the problem can be solved efficiently. Further, we introduce an integer linear program to address the problem for networks of reasonable sizes. For solving the problem on larger networks, we propose a general heuristic framework along with greedy selection methods. Extensive experimental results on real-world networks demonstrate the effectiveness of the proposed heuristics. A full version of the manuscript, source code and data are available at: https://github.com/bridgelessqiu/NMIN-FPE

There are myriad real-life examples of contagion processes on human social networks, e.g., spread of viruses, information, and social unrest. Also, there are many methods to control or block contagion spread. In this work, we introduce a novel method of blocking contagions that uses nodes from dominating sets (DSs). To our knowledge, this is the first use of DS nodes to block contagions. Finding minimum dominating sets of graphs is an NP-Complete problem, so we generalize a well-known heuristic, enabling us to customize its execution. Our method produces a prioritized list of dominating nodes, which is, in turn, a prioritized list of blocking nodes. Thus, for a given network, we compute this list of blocking nodes and we use it to block contagions for all blocking node budgets, contagion seed sets, and parameter values of the contagion model. We report on computational experiments of the blocking efficacy of our approach using two mined networks. We also demonstrate the effectiveness of our approach by comparing blocking results with those from the high degree heuristic, which is a common standard in blocking studies.

Motivated by a wide range of applications, research on agent-based models of contagion propagation over networks has attracted a lot of attention in the literature. Many of the available software systems for simulating such agent-based models require users to download software, build the executable, and set up execution environments. Further, running the resulting executable may require access to high performance computing clusters. Our work describes an open access software system (NetSimS) that works under the “Modeling and Simulation as a Service” (MSaaS) paradigm. It enables users to run simulations by selecting models and parameter values, initial conditions, and networks through a web interface. The system supports a variety of models and networks with millions of nodes and edges. In addition to the simulator, the system includes components that enable users to choose initial conditions for simulations in a variety of ways, to analyze the data generated through simulations, and to produce plots from the data. We describe the components of NetSimS and carry out a performance evaluation of the system. We also discuss two case studies carried out on large networks using the system. NetSimS is a major component within net.science, a cyberinfrastructure for network science.

Using a discrete dynamical system model, many papers have addressed the problem of learning the behavior (i.e., the local function at each node) of a networked system through active queries, assuming that the network topology is known. We address the problem of inferring both the topology of the network and the behavior of a discrete dynamical system through active queries. We consider two query models studied in the literature, namely the batch model (where all the queries must be submitted together) and the adaptive model (where responses to previous queries can be used in formulating a new query). Our results are for systems where the state of each node is from {0,1} and the local functions are Boolean. We present algorithms to learn the topology and the behavior under both batch and adaptive query models for several classes of dynamical systems. These algorithms use only a polynomial number of queries. We also present experimental results obtained by running our query generation algorithms on synthetic and real-world networks.

Developing techniques to infer the behavior of networked social systems has attracted a lot of attention in the literature. Using a discrete dynamical system to model a networked social system, the problem of inferring the behavior of the system can be formulated as the problem of learning the local functions of the dynamical system. We investigate the problem assuming an active form of interaction with the system through queries. We consider two classes of local functions (namely, symmetric and threshold functions) and two interaction modes, namely batch (where all the queries must be submitted together) and adaptive (where the set of queries submitted at a stage may rely on the answers to previous queries). We establish bounds on the number of queries under both batch and adaptive query modes using vertex coloring and probabilistic methods. Our results show that a small number of appropriately chosen queries are provably sufficient to correctly learn all the local functions. We develop complexity results which suggest that, in general, the problem of generating query sets of minimum size is computationally intractable. We present efficient heuristics that produce query sets under both batch and adaptive query modes. Also, we present a query compaction algorithm that identifies and removes redundant queries from a given query set. Our algorithms were evaluated through experiments on over 20 well-known networks.

Discrete dynamical systems serve as useful formal models to study diffusion phenomena in social networks. Motivated by applications in systems biology, several recent papers have studied algorithmic and complexity aspects of diffusion problems for dynamical systems whose underlying graphs are directed, and may contain directed cycles. Such problems can be regarded as reachability problems in the phase space of the corresponding dynamical system. We show that computational intractability results for reachability problems hold even for dynamical systems on directed acyclic graphs (dags). We also show that for dynamical systems on dags where each local function is monotone, the reachability problem can be solved efficiently.

We explore the area of fairness in clustering from the different perspective of modifying clusterings from existing algorithms to make them fairer whilst retaining their quality. We formulate the minimal cluster modification for fairness (MCMF) problem where the input is a given partitional clustering and the goal is to minimally change it so that the clustering is still of good quality and fairer. We show using an intricate case analysis that for a single protected variable, the problem is efficiently solvable (i.e., in the class P) by proving that the constraint matrix for an integer linear programming (ILP) formulation is totally unimodular (TU). Interestingly, we show that even for a single protected variable, the addition of simple pairwise guidance (to say ensure individual level fairness) makes the MCMF problem computationally intractable (i.e., NP-hard). Experimental results on Twitter, Census and NYT data sets show that our methods can modify existing clusterings for data sets in excess of 100,000 instances within minutes on laptops and find as fair but higher quality clusterings than fair by design clustering algorithms.

Improving the explainability of the results from machine learning methods has become an important research goal. Here, we study the problem of making clusters more interpretable by extending a recent approach of [Davidson et al., NeurIPS 2018] for constructing succinct representations for clusters. Given a set of objects S, a partition π of S (into clusters), and a universe T of tags such that each element in S is associated with a subset of tags, the goal is to find a representative set of tags for each cluster such that those sets are pairwise-disjoint and the total size of all the representatives is minimized. Since this problem is NP-hard in general, we develop approximation algorithms with provable performance guarantees for the problem. We also show applications to explain clusters from datasets, including clusters of genomic sequences that represent different threat levels.

Fear of reprisals such as violence and punishment can inhibit citizens from speaking out, or make them more reluctant to act, in opposition to a repressive regime. Protests are one form of opposition, and their growth has been successfully modeled as an inftuence-based contagion process within a social network (representing a population). In these models, an individual joins a protest if a sufficient number of her neighbors has already joined. This required number of neighbors is often called a “threshold.” In this study, we model a regime's ability to suppress protests by instilling fear in a subset of a population, and this fear is manifested by an increase in a person's threshold. We consider different social networks, numbers of seed nodes, and amounts of fear. Through simulations, we present several results. For example, we demonstrate that, for the objective of reducing the size of a protest, inducing fear can be more advantageous than removing nodes from a network.

In Boolean games, each agent controls a set of Boolean variables and has a goal represented by a propositional formula. We study inference problems in Boolean games assuming the presence of a PRINCIPAL who has the ability to control the agents and impose taxation schemes. Previous work used taxation schemes to guide a game towards certain equilibria. We present algorithms that show how taxation schemes can also be used to infer agents' goals. We present experimental results to demonstrate the efficacy our algorithms. We also consider goal inference when only limited information is available in response to a query.

Active learning aims to reduce labeling efforts by selectively asking humans to annotate the most important data points from an unlabeled pool and is an example of human-machine interaction. Though active learning has been extensively researched for classification and ranking problems, it is relatively understudied for regression problems. Most existing active learning for regression methods use the regression function learned at each active learning iteration to select the next informative point to query. This introduces several challenges such as handling noisy labels, parameter uncertainty and overcoming initially biased training data. Instead, we propose a feature-focused approach that formulates both sequential and batch-mode active regression as a novel bipartite graph optimization problem. We conduct experiments on both noise-free and noisy settings. Our experimental results on benchmark data sets demonstrate the effectiveness of our proposed approach.

Controller area network (CAN) is a widely used protocol that allows communication among electronic control units (ECUs) in automotive electronics. It was extended to CAN with flexible data-rate (CAN-FD) to meet the increasing demand for bandwidth generated by the growing number of features in modern automobiles. The signal-to-frame packing problem has been studied in the literature for both CAN and CAN-FD. In this paper, we propose and formulate the signal offset assignment problem (SOAP) in CAN-FD to improve the bus utilization during frame packing. We propose two algorithmic themes to solve SOAP and establish their worst case performance guarantees. The first is a general approximation framework (GAF) which can use any approximation algorithm for the makespan minimization problem (MMP) in multiprocessor systems. Its performance guarantee is the product of the performance guarantee of the MMP algorithm and the number of distinct periods in the frame. The second is a 2-D strip packing-based framework (2DSPF) which uses the bottom left fill algorithm for 2-D strip packing. The performance guarantee is 2G , where G is the minimum number of groups into which the set of signals can be partitioned so that the periods of the signals in the same group form a geometric series. The experimental results for GAF and 2DSPF indicate that by carefully assigning offsets for signals in frame packing schemes, one can achieve about 10.83% improvement in bus utilization in CAN-FD systems.

In anagram games, players are provided with letters for forming as many words as possible over a specified time duration. Anagram games have been used in controlled experiments to study problems such as collective identity, effects of goal-setting, internal-external attributions, test anxiety, and others. The majority of work on anagram games involves individual players. Recently, work has expanded to group anagram games where players cooperate by sharing letters. In this work, we analyze experimental data from online social networked experiments of group anagram games. We develop mechanistic and data-driven models of human decision-making to predict detailed game player actions (e.g., what word to form next). With these results, we develop a composite agent-based modeling and simulation platform that incorporates the models from data analysis. We compare model predictions against experimental data, which enables us to provide explanations of human decision-making and behavior. Finally, we provide illustrative case studies using agent-based simulations to demonstrate the efficacy of models to provide insights that are beyond those from experiments alone.

We consider the PAC learnability of the local functions at the vertices of a discrete networked dynamical system, assuming that the underlying network is known. Our focus is on the learnability of threshold functions. We show that several variants of threshold functions are PAC learnable and provide tight bounds on the sample complexity. In general, when the input consists of positive and negative examples, we show that the concept class of threshold functions is not efficiently PAC learnable, unless NP = RP. Using a dynamic programming approach, we show efficient PAC learnability when the number of negative examples is small. We also present an efficient learner which is consistent with all the positive examples and at least (1-1/e) fraction of the negative examples. This algorithm is based on maximizing a submodular function under matroid constraints. By performing experiments on both synthetic and real-world networks, we study how the network structure and sample complexity influence the quality of the inferred system.

Abduction is an inference approach that uses data and observations to identify plausible (and preferably, best) explanations for phenomena. Applications of abduction (e.g., robotics, genetics, image understanding) have largely been devoid of human behavior. Here, we devise and execute an iterative abductive analysis process that is driven by the social sciences: behaviors and interactions among groups of human subjects. One goal is to understand intra-group cooperation and its effect on fostering collective identity. We build an online game platform; perform and analyze controlled laboratory experiments; form hypotheses; build, exercise, and evaluate network-based agent-based models; and evaluate the hypotheses in multiple abductive iterations, improving our understanding as the process unfolds. While the experimental results are of interest, the paper's thrust is methodological, and indeed establishes the potential of iterative abductive looping for the (computational) social sciences.

The problem of inferring unknown parameters of a networked social system is of considerable practical importance. We consider this problem for the independent cascade model using an active query framework. More specifically, given a network whose edge probabilities are unknown, the goal is to infer the probability value on each edge by querying the system. The optimization objective is to use as few queries as possible in carrying out the inference. We present approximation algorithms that provide provably good estimates of edge probabilities. We also present results from an experimental evaluation of our algorithms on several real-world networks.

In many settings just finding a good clustering is insufficient and an explanation of the clustering is required. If the features used to perform the clustering are interpretable then methods such as conceptual clustering can be used. However, in many applications this is not the case particularly for image, graph and other complex data. Here we explore the setting where a set of interpretable discrete tags for each instance is available. We formulate the descriptive clustering problem as a bi-objective optimization to simultaneously find compact clusters using the features and to describe them using the tags. We present our formulation in a declarative platform and show it can be integrated into a standard iterative algorithm to find all Pareto optimal solutions to the two objectives. Preliminary results demonstrate the utility of our approach on real data sets for images and electronic health care records and that it outperforms single objective and multi-view clustering baselines.

Consider the situation where you are given an existing $k$-way clustering $\pi$. A challenge for explainable AI is to find a compact and distinct explanations of each cluster which in this paper is using instance-level descriptors/tags from a common dictionary. Since the descriptors/tags were not given to the clustering method, this is not a semi-supervised learning situation. We show that the \emph{feasibility} problem of just testing whether any distinct description (not the most compact) exists is generally intractable for just two clusters. This means that unless \textbf{P} = \cnp, there cannot exist an efficient algorithm for the cluster description problem. Hence, we explore ILP formulations for smaller problems and a relaxed but restricted setting that leads to a polynomial time algorithm for larger problems. We explore several extension to the basic setting such as the ability to ignore some instances and composition constraints on the descriptions of the clusters. We show our formulation's usefulness on Twitter data where the communities were found using social connectivity (i.e. \texttt{follower} relation) but the explanation of the communities is based on behavioral properties of the nodes (i.e. hashtag usage) not available to the clustering method.

Consider the situation where your favorite clustering algorithm applied to a data set returns a good clustering but there are a few undesirable properties. One adhoc way to fix this is to re-run the clustering algorithm and hope to find a better variation. Instead, we propose to not run the algorithm again but minimally modify the existing clustering to remove the undesirable properties. We formulate the minimal clustering modification problem where we are given an initial clustering produced from any algorithm. The clustering is then modified to: i) remove the undesirable properties and ii) be minimally different to the given clustering. We show the underlying feasibility sub-problem can be intractable and demonstrate the flexibility of our constraint programming formulation. We empirically validate its usefulness through experiments on social network and medical imaging data sets.

The Controller Area Network with Flexible Data-Rate (CAN-FD) is a new communication protocol to meet the bandwidth requirements for the constantly growing volume of data exchanged in modern vehicles. The problem of frame packing for CAN-FD, as studied in the literature, assumes a single sub-system where one CAN-FD bus serves as the communication medium among several Electronic Control Units (ECUs). Modern automotive electronic systems, on the other hand, consist of several sub-systems, each facilitating a certain functional domain such as powertrain, chassis and suspension. A substantial fraction of all signals is exchanged across sub-systems. In this work, we study the frame packing problem for CAN-FD with multiple sub-systems, and propose a two-stage optimization framework. In the first stage, we pack the signals into frames with the objective of minimizing the bandwidth utilization. In the second stage, we extend Audsley's algorithm to assign priorities/identifiers to the frames. In case the resulting solution is not schedulable, our framework provides a potential repacking method. We propose two solution approaches: (a) an Integer Linear Programming (ILP) formulation that provides an optimal solution but is computationally expensive for industrial-size problems; and (b) a greedy heuristic that scales well and provides solutions that are comparable to optimal solutions. Experimental results show the efficiency of our optimization framework in achieving feasible solutions with low bandwidth utilization. The results also show a significant improvement over the case when there is no cross-domain consideration (as in prior work).

Controller Area Network (CAN) is a widely used protocol that allows communication among Electronic Control Units (ECUs) in automotive electronics. It was extended to CAN-FD (CAN with Flexible Data-rate) to meet the increasing demand for bandwidth utilization caused by the growing number of features in modern automobiles. The signal-to-frame packing problem has been studied in literature for both CAN and CAN-FD. In this work, we propose and formulate, for the first time, the signal offset assignment problem (SOAP) in a frame in order to improve the bus bandwidth utilization. We prove that SOAP is NP-complete. We propose a general approximation framework (GAF) for SOAP which can use any approximation algorithm for the makespan minimization problem (MMP) in multiprocessor systems. We derive the performance guarantee provided by GAF as a function of the performance guarantee of the approximation algorithm for MMP and the number of signal periods in the frame. We demonstrate the efficacy of our approach through experiments using three different algorithms (two approximation algorithms and an integer linear programming formulation) for MMP in GAF. Our results indicate that by using offsets for signals in frame packing schemes, one can achieve about 10.54% improvement in bandwidth utilization (on a single bus) in CAN-FD systems.

In modern System on Chip (SoC) designs, the multi-cycle delays on long interconnects between synchronously clocked IP blocks are accommodated by latency insensitive protocols (LIP) through extra valid/stall handshakes between components and additional logic blocks called relay stations. The use of handshaking interconnects and relay stations leads to area and latency penalties, that must be minimized for cost effective SoC designs. Interconnected IP blocks with certain graph topology have periodic behaviors that can be exploited to remove the need for handshake interconnects. Unfortunately, the periodic schedule may not exist for any LIS designs consist of two or more strongly connected components. Some of these systems are not bounded without back pressure. In the past, back pressure between SCCs has always been implemented as stall signals in the backward direction, and they are required to prevent overflow. In this paper, we propose an LIS design optimization algorithm which computes a minimum set of back pressure arcs required between SCCs. We model an LIS by a partial back pressure graph (PBPG) and show that the boundedness of a PBPG can be verified by checking the reachability in its strongly connected component graph (SCCG). Based on this, we formulate the problem of finding a minimum set of back pressure arcs (MBPA) and show that this problem can be reduced to the Minimum Cost Arborescence (MCA) problem for directed graphs. This allows us to obtain a polynomial time algorithm for synthesizing a minimum cost LIS implementation starting from a synchronous model of the original system. After adding back pressure arcs, we develop a localized Mixed Integer Linear Programming (LMILP) approach to optimize the throughput of the resulting LIS. This approach scales better than existing MILP-based throughput optimization techniques. In addition, we also provide an implementation of the LIS which refines its PBPG model. To the best of our knowledge, this is the first effort that considers the optimization of back pressure and throughput together in the design of latency insensitive systems.

Analysis of structural properties and dynamics of networks is currently a central topic in many disciplines including Social Sciences, Biology and Business. CINET, a cyber infrastructure for such studies, introduced the concept of supporting network analysis as a service. The basic idea is to allow experts in various disciplines to focus on obtaining domain-specific insights from the results of network analyses instead of worrying about programming details and allocation of computational resources needed to carry out the analyses. A basic version of CINET was released in May 2012. This paper discusses CINET 2.0, a significantly enhanced version that supports complex network analyses through a web portal. CINET 2.0 has already been used for teaching courses related to Network Science at several US universities. In this paper, we discuss how CINET 2.0 significantly extends CINET 1.0 through enhancements to some components and the addition of new components.

Trajectory compression algorithms enable efficient transmission, storage, and processing of trajectory data by eliminating redundant information. While a large number of compression algorithms have been developed, there is no comprehensive and convenient benchmarking system for evaluating these algorithms. We will demonstrate TrajMetrix, our system that meets the above need. We will show how TrajMetrix can be used to gain insights into the benefits and drawbacks of various compression algorithms given different compression requirements.

Eliminating interactions among individuals is an important means of blocking contagion spread, e.g., closing schools during an epidemic or shutting down electronic communication channels during social unrest. We study contagion blocking in networked populations by identifying edges to remove from a network, thus blocking contagion transmission pathways. We formulate various problems to minimize contagion spread and show that some are efficiently solvable while others are formally hard. We also compare our hardness results to those from node blocking problems and show interesting differences between the two. Our main problem is not only hard, but also has no approximation guarantee, unless P=NP. Therefore, we devise a heuristic for the problem and compare its performance to state-of-the-art heuristics from the literature. We show, through results of 12 (network, heuristic) combinations on three real social networks, that our method offers considerable improvement in the ability to block contagions in weighted and unweighted networks. We also conduct a parametric study to understand the limitations of our approach.

Networks are an effective abstraction for representing real systems. Consequently, network science is increasingly used in academia and industry to solve problems in many fields. Computations that determine structure properties and dynamical behaviors of networks are useful because they give insights into the characteristics of real systems. We introduce a newly built and deployed cyberinfrastructure for network science (CINET) that performs such computations, with the following features: (i) it offers realistic networks from the literature and various random and deterministic network generators; (ii) it provides many algorithmic modules and measures to study and characterize networks; (iii) it is designed for efficient execution of complex algorithms on distributed high performance computers so that they scale to large networks; and (iv) it is hosted with web interfaces so that those without direct access to high performance computing resources and those who are not computing experts can still reap the system benefits. It is a combination of application design and cyberinfrastructure that makes these features possible. To our knowledge, these capabilities collectively make CINET novel. We describe the system and illustrative use cases, with a focus on the CINET user.

Network longevity and connectivity are key design goals in any wireless sensor network deployment. In this context, we consider the placement of relay nodes and individual transmission power assignments. Specifically, given a planar deployment of sensors and a base station, we seek the placement of a limited number of relays and optimal sensor power assignments such that the network is connected. We present a polynomial-time bicriteria approximation algorithm for this problem. We also provide an optimal O(n2log n)-time algorithm for a restricted version where nodes lie on a simplified urban grid (that we call a comb-grid). We also study a related variant that assumes fixed transmission power values, with the goal of minimizing the number of relays. We provide extensive simulation results for the comb-grid case.

In this paper we advocate the study of discrete models of social dynamics underadversarial scheduling. The approach we propose forms part of a foundational basis for agenerative approach to social science(Epstein 2007). We highlight the feasibility of the adversarial scheduling approach by using it to study thePrisoners's Dilemma Game with Pavlov update, a dynamics that has already been investigated under random update in Kittock (1994), Dyeret al. (2002), Mossel and Roch (2006) and Dyer and Velumailum (2011). The model is specified by letting players at the nodes of an underlying graphGrepeatedly play the Prisoner's Dilemma against their neighbours. The players adapt their strategies based on the past behaviour of their opponents by applying the so-called win–stay lose–shift strategy. With random scheduling, starting from any initial configuration, the system reaches the fixed point in which all players cooperate with high probability. On the other hand, under adversarial scheduling the following results hold: — A scheduler that can selectbothgame participants can preclude the system from reaching the unique fixed point on most graph topologies. — A non-adaptive scheduler that is only allowed to chooseoneof the participants is no more powerful than a random scheduler. With this restriction, even an adaptive scheduler is not significantly more powerful than the random scheduler, provided it is ‘reasonably fair’.

The massive volumes of trajectory data generated by inexpensive GPS devices have led to difficulties in processing, querying, transmitting and storing such data. To overcome these difficulties, a number of algorithms for compressing trajectory data have been proposed. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. We present results from a comprehensive empirical evaluation of many compression algorithms including Douglas-Peucker Algorithm, Bellman's Algorithm, STTrace Algorithm and Opening Window Algorithms. Our empirical study uses different types of real-world data such as pedestrian, vehicle and multimodal trajectories. The algorithms are compared using several criteria including execution times and the errors caused by compressing spatio-temporal information, across numerous real-world datasets and various error metrics.

Most scheduling based latency insensitive designs in the literature focus on systems whose graphical representation is a single strongly connected component (SCC), where a hand-shake based protocol can be replaced by periodic clock gating through ASAP scheduling. However, for systems that are represented as interconnected SCCs, `back pressure', always implemented as the `stall' signal in the backward directions between SCCs, is required to prevent overflow. In this paper, we formulate the problem of finding a minimum set of back pressure edges. We show that this problem can be reduced to the Minimum Cost Arborescence (MCA) problem for directed graphs. This allows us to obtain a polynomial time algorithm for synthesizing a minimum cost latency insensitive implementation starting from a synchronous model of the original system. We also show that implementing back pressure edges for every inter-SCC connection, as done in a regular hand-shake based protocol, is inferior for the overall system's throughput. Our approach provides a formal framework for converting a synchronous model into a latency insensitive implementation with a minimum number of inter-SCC back pressure edges and for leveraging periodic clock based scheduling of intra-SCC latency insensitivity.

The area of clustering under constraints has recently received much attention in the data mining community. However, most work involves adding constraints to existing algorithms which, although being quite pragmatic, raises several difficulties. Examples of these difficulties include creating intractable constraint satisfaction sub-problems and constrained clustering algorithms that are easily over-constrained so they may not converge or converge to a poor clustering solution. In this paper we show how both instance and cluster-level constraints can be expressed as instances of the 2SAT problem and how multiple calls to a 2SAT solver can be used to construct algorithms that are guaranteed to satisfy all the constraints and converge to a global optimum for a number of intuitive objective functions. Our approach provides two additional advantages. Firstly, it leads to polynomial time algorithms for the k = 2 case for several objective functions. Secondly, one can specify large sets of constraints without fear of over-constraining the problem: if one or more solutions satisfying all constraints exist, our algorithm is guaranteed to find a best such solution. We present experimental results to show that our approach outperforms several popular algorithms particularly for large constraint sets, where these algorithms are over-constrained and fair poorly.

Topology control in ad hoc networks is a multi-criteria optimization problem involving (contradictory) objectives of connectivity, interference, and power minimization. Additionally, nodes can be unreliable, which adds another dimension to an already challenging problem. In this paper, we study topology control problems in ad hoc networks under node failures for arbitrary node distributions. We consider a simple and natural stochastic failure model, in which each node can fail independently with a given probability. The topology control problem under stochastic failures is to choose a power level for each node and a subset of edges such that the residual graph (i.e., the graph formed by the nodes which have not failed) is connected and can be scheduled efficiently, with high probability. We develop provably efficient bi-criteria approximation algorithms for this problem that simultaneously minimize power, reduce interference, and ensure that the surviving graph is connected with high probability. Our algorithms can be implemented efficiently in a distributed manner.

We develop a graph-theoretic model for service-oriented networks and propose metrics that quantify the resilience of such networks under node and edge failures. These metrics are based on the topological structure of the network and the manner in which services are distributed over the network. We present efficient algorithms to determine the maximum number of node and edge failures that can be tolerated by a given service-oriented network. These algorithms rely on known algorithms for computing minimum cuts in graphs. We also present efficient algorithms for optimally allocating services over a given network so that the resulting service-oriented network can tolerate single node or edge failures. These algorithms are derived through a careful analysis of the decomposition of the underlying network into appropriate types of connected components.

Clustering with constraints is a developing area of machine learning. Various papers have used constraints to enforce particular clusterings, seed clustering algorithms and even learn distance functions which are then used for clustering. We present intractability results for some constraint combinations and illustrate both formally and experimentally the implications of these results for using constraints with clustering.

Clustering with constraints is an emerging area of data mining research. However, most work assumes that the constraints are given as one large batch. In this paper we explore the situation where the constraints are incrementally given. In this way the user after seeing a clustering can provide positive and negative feedback via constraints to critique a clustering solution. We consider the problem of efficiently updating a clustering to satisfy the new and old constraints rather than reclustering the entire data set. We show that the problem of incremental clustering under constraints is NP-hard in general, but identify several sufficient conditions which lead to efficiently solvable versions. These translate into a set of rules on the types of constraints thatcan be added and constraint set properties that must be maintained. We demonstrate that this approach is more efficient than re-clustering the entire data set and has several other advantages.

Controlling the topology of a wireless ad hoc network is very important from the point of view of performance. One known technique for controlling the topology is through the assignment of appropriate transmission power levels to the nodes. Such an assignment aims to minimize a specified function of the powers assigned to nodes. While this problem has been widely studied for the case of stationary wireless networks, few reported theoretical results for mobile wireless networks (MANETs). In this paper, we consider the topology control problem for MANETs from a theoretical perspective. We define a topology control problem under the constant rate mobile network model. In this model, all the n nodes in the network may move. Associated with each moving node are its constant moving speed and direction. The goal is to minimize the maximum power used by any network node in producing a connected network. We provide two polynomial algorithms for solving this problem: one for the decision version, the other for the optimization version.

Topology control is the problem of assigning power levels to the nodes of an ad hoc network so as to create a specified network topology while minimizing the energy consumption of the network nodes. While considerable theoretical attention has been given to the issue of topology control in wireless ad hoc networks, all of that work has concerned stationary networks. In this paper we carry out a theoretical study of a topology control problem in mobile wireless ad hoc networks (MANETs). For MANETs, we define a topology control problem under the Simple Mobile Network model, where there is one moving node and n stationary nodes. The goal is to minimize the maximum power used by any node in producing a connected network. We provide three polynomial algorithms for solving this problem. Surprisingly, the fastest algorithm runs as fast as the best known algorithm for stationary networks.

Abstract Facility dispersion problems arise in the context of placing obnoxious facilities and retail outlets. In the offline version of such a problem, the input consists of a complete graph on n nodes, a nonnegative weight (distance) for each edge, and the number k ⩽ n of facilities to be placed. The goal is to choose a facility placement consisting of k nodes so as to maximize a given measure of the distances among the facilities. Here, we consider an online version of the problem where the value of k is not known apriori; instead, requests for facilities arrive one at a time. It is also required that previously placed facilities cannot be moved or eliminated. Our main result is that for any objective that satisfies two properties, namely monotonicity and graceful degradation, any offline approximation algorithm with a performance guarantee ρ can be used to develop an algorithm with competitive ratio c ρ for the online version, where c is a constant independent of the problem instance. Objectives for which our result applies include the average edge weight and average weight of a star subgraph. The result holds even when the edge weights do not satisfy the triangle inequality. We also identify dispersion objectives for which the offline and online versions have different behaviors when only one of the above two properties is satisfied. © 2006 Wiley Periodicals, Inc. NETWORKS, Vol. 47(4), 206–217 2006

Recent work has looked at extending the k-Means algorithm to incorporate background information in the form of instance level must-link and cannot-link constraints. We introduce two ways of specifying additional background information in the form of δ and ∊ constraints that operate on all instances but which can be interpreted as conjunctions or disjunctions of instance level constraints and hence are easy to implement. We present complexity results for the feasibility of clustering under each type of constraint individually and several types together. A key finding is that determining whether there is a feasible solution satisfying all constraints is, in general, NP-complete. Thus, an iterative algorithm such as k-Means should not try to find a feasible partitioning at each iteration. This motivates our derivation of a new version of the k-Means algorithm that minimizes the constrained vector quantization error but at each iteration does not attempt to satisfy all constraints. Using standard UCI datasets, we find that using constraints improves accuracy as others have reported, but we also show that our algorithm reduces the number of iterations until convergence. Finally, we illustrate these benefits and our new constraint types on a complex real world object identification problem using the infra-red detector on an Aibo robot.

There has recently been renewed interest among various research communities in understanding the structure of social and infrastructure networks. Motivated by this line of research, we conduct an in-depth structural analysis of large ad hoc networks derived by placing nodes randomly as well as by placing them in realistic urban environments, a scenario that is rapidly gaining interest (K. Jain, et al, 2003). We use structural analysis in two illustrative settings. First, we use it to study the performance of network protocols. Our results indicate that structural analysis of interference graphs that model ad hoc networks can yield a good first order prediction of the overall protocol performance. Second, we study the robustness of a network to random node and edge failures. This study is important in the context of ad hoc networks wherein one expects nodes/edges to fail due various natural or system dependent reasons. The experimental results presented in this paper show the following: (i) structural properties of ad hoc networks depend crucially on the spatial distribution of the nodes. (ii) Structural properties of the network significantly affect the performance of protocols. (iii) Graph theoretic measures can provide good first order insights into the network protocol performance. (iv) The measures are also useful in characterizing the robustness of such networks.

Topology control problems are concerned with the assignment of power values to the nodes of an ad hoc network so that the power assignment leads to a graph topology satisfying some specified properties. This paper considers such problems under several optimization objectives, including minimizing the maximum power and minimizing the total power. A general approach leading to a polynomial algorithm is presented for minimizing maximum power for a class of graph properties called textbf monotone properties. The difficulty of generalizing the approach to properties that are not monotone is discussed. Problems involving the minimization of total power are known to be bf NP -complete even for simple graph properties. A general approach that leads to an approximation algorithm for minimizing the total power for some monotone properties is presented. Using this approach, a new approximation algorithm for the problem of minimizing the total power for obtaining a 2-node-connected graph is obtained. It is shown that this algorithm provides a constant performance guarantee. Experimental results from an implementation of the approximation algorithm are also presented.

Placement of inspection stations is a common task in transportation and communication networks. In this paper, two categories of problems involving placement of inspection stations are studied. The first category deals with the selection of inspection stations along a given path from an origin to a destination. The second considers simultaneous selection of both a path and inspection stations along that path. We formulate these problems under a variety of minimization objectives such as the maximum gap between two consecutive inspection stations, the expected penalty cost of failure along the path, and the total inspection cost. Our results include efficient algorithms for many formulations and complexity results as well as fully polynomial approximation schemes for other formulations. When considering cost objectives, we identify a core problem and show that the complexity of many formulations is directly related to the complexity of the core problem.

We consider path problems in networks where each edge is associated with a vector of weights. One application where such path problems arise is in transporting hazardous materials. In that context, the network is embedded in a cluster of communities (or zones), and it is important to consider the impact of an accident along an edge on the surrounding zones. This impact is modeled as a cost vector for each edge, where each component represents the impact of an accident on a zone. Under this model, we formulate two kinds of path problems, namely, routing and feasibility problems. These formulations utilize various definitions of equity with respect to cost impact on the zones. We present complexity results and pseudopolynomial algorithms for general versions as well as efficient algorithms for special cases. We also carry out a comparative analysis of different routing problems. © 1999 John Wiley & Sons, Inc. Networks 34: 19–35, 1999

Commercial distributed database systems generally support an optional protocol that provides loose consistency of replicas, allowing replicas to be inconsistent for some time. In such a protocol, each replicated data item is assigned a primary copy site. Typically, a transaction updates only the primary copies of data items, with updates to other copies deferred until after the transaction commits. After a transaction commits, its updates to primary copies are sent transactionally to the other sites containing secondary copies. We investigate the transaction model underlying the above protocol. We show that global serializability in such a system is a property of the placement of primary and secondary copies of replicated data items. We present a polynomial time algorithm to assign primary sites to data items so that the resulting topology ensures serializability.

No abstract available.

We consider linear interval routing schemes studied by [3,5] from a graph-theoretical perspective. We examine how the number of linear intervals needed to obtain shortest path routings in networks is affected by the product, join and composition operations on graphs. This approach allows us to generalize some of the results of [3,5] concerning the minimum number of intervals needed to achieve shortest path routings in certain special classes of networks. We also establish an Ω(n1/3) lower bound on the minimum number of intervals needed to achieve shortest path routings in the network considered.

We study the problem of finding small trees. Classical network design problems are considered with the additional constraint that only a specified number k of nodes are required to be connected in the solution. A prototypical example is the kMST problem in which we require a tree of minimum weight spanning at least k nodes in an edge-weighted graph. We show that the kMST problem is NP-hard even for points in the Euclidean plane. We provide approximation algorithms with performance ratio $2\sqrt{k} $ for the general edge-weighted case and $O(k^{1/4} )$ for the case of points in the plane. Polynomial-time exact solutions are also presented for the class of treewidth-bounded graphs, which includes trees, series-parallel graphs, and bounded bandwidth graphs, and for points on the boundary of a convex region in the Euclidean plane. We also investigate the problem of finding short trees and, more generally, that of finding networks with minimum diameter. A simple technique is used to provide a polynomial-time solution for finding k-trees of minimum diameter. We identify easy and hard problems arising in finding short networks using a framework due to T. C. Hu.

No abstract available.

Abstract Unit disk graphs are intersection graphs of circles of unit radius in the plane. We present simple and provably good heuristics for a number of classical NP‐hard optimization problems on unit disk graphs. The problems considered include maximum independent set, minimum vertex cover, minimum coloring, and minimum dominating set. We also present an on‐line coloring heuristic which achieves a competitive ratio of 6 for unit disk graphs. Our heuristics do not need a geometric representation of unit disk graphs. Geometric representations are used only in establishing the performance guarantees of the heuristics. Several of our approximation algorithms can be extended to intersection graphs of circles of arbitrary radii in the plane, intersection graphs of regular polygons, and intersection graphs of higher dimensional regular objects.

Algorithm-based fault tolerance (ABFT) is a popular approach to achieve fault and error detection in multiprocessor systems. The design problem for ABFT is concerned with the construction of a check set of minimum cardinality that detects a specified number of errors or faults. Previous work on this problem has assumed an a priori bound on the size of a check. We motivate and carry out an investigation of the problem without the bounded check size assumption. We establish upper and lower bounds on the number of checks needed to detect a given number of errors. The upper bounds are obtained through new schemes which are easy to implement, and the lower bounds are established using new types of arguments. These bounds are sharply different from those previously established under the bounded check size model. We also show that unlike error detection, the design problem for fault detection is NP-hard even for detecting only one fault.>

We study network-design problems with multiple design objectives.In particular, we look at two cost NY 12222.

Lower and upper bounds are established for the combinatorial problem of constructing minimal test sets for error detection in multiprocessor systems. The construction for detecting two errors produces minimal test sets, while that for three errors produces test sets whose size exceeds the lower bound by at most one. Also presented is a divide-and-conquer construction scheme for four or more errors.>

Functional cells are a physical realization of complex MOS gates. Efficient algorithms for minimizing the width of a functional cell are known. Every solution to the width minimization problem leads to a cell of a certain height. It is shown that, even for functional cells of complex MOS gates represented by series-parallel transistor networks, the problem of finding a solution of minimum width that also minimizes the height is NP-hard.>

A sequence of input vectors which detects all transistor stuck-open faults in a CMOS combinational circuit is a complete test sequence. Given a complete set of two-pattern tests for transistor stuck-open faults in a CMOS circuit, it is shown that a complete test sequence of minimum length can be obtained efficiently. A precise description of this problem and examples to illustrate the method are presented.>

The authors show that the problem of obtaining a minimum complete test set is NP-complete for monotone PLAs even when each product term of the PLA contains at most two literals. Using the ideas developed in the proof of this result, they resolve an open question due to B. Krishnamurthy and S.B. Akers (1984). The authors also show that given a complete test set T, the problem of obtaining a minimum test set contained in T is NP-complete even for two-level monotone circuits.>

The problem of optimally folding a Programmable Logic Array (PLA) is known to be NP-complete. Motivated by the practical importance of this problem, we address the question of obtaining good, though not necessarily optimal, foldings. Two sets of results are presented. First, we show that three natural variants of the folding problem are equivalent with respect to approximation, in the sense that either they are all efficiently approximable or none of them is efficiently approximable. Next, we show for one of the variants (optimal bipartite folding) that if there is a polynomial time approximation algorithm (heuristic) which, for every PLA, produces a folding that is within a fixed factor of an optimal folding, then for any constant $\varepsilon > 0$, there is a heuristic which, for every PLA, produces a folding that is within a factor of $(1+\varepsilon )$ of the optimal folding. This result strongly suggests that the optimal folding problem is not efficiently approximable for arbitrary PLAs. In a companion paper, we have presented efficient heuristics for certain restricted classes of PLAs.