Electric vehicles have become a trend as a replacement to gasoline-powered vehicles, and been promoted by worldwide policy makers as a solution to combat environmental problems and stimulate economy, whereas the lack of extreme fast charging infrastructure has become one main obstacle to broad adoption of electric vehicles. To promote the commercial success of electric vehicles, effective placement of electric vehicle (EV) charging stations is pivotal. While numerous studies address EV charging station placement, the integration of transportation network traffic, specifically equilibrium traffic assignment, where flows stabilize as drivers seek routes to minimize travel time, has been relatively limited. This research investigates equilibrium traffic assignment with the inclusion of extreme fast charging (XFC) stations and introduces an algorithmic solution. We assess diverse charging station placement strategies, including node-based and network-based approaches, weighing their respective advantages and drawbacks. Extensive experiments on real transportation networks of varying scales validate our algorithm and evaluate different charging station placement strategies. Many interesting findings are drawn from the study. For instance, increasing the number of XFC charging stations may not always result in reduced traffic time; the added value of extra stations beyond a certain threshold can be quite limited. The findings offer valuable insights for strategically deploying EV charging infrastructure, thus promoting electric vehicle adoption.

Influence maximization(IM) aims to identify a set of nodes$S$to maximize the expected number of nodes influenced during the information propagation starting from$S$. Some works had extended this problem to betopic-aware, where each node is associated with a topic distribution and tends to be activated with different probabilities by different topics. However, whether it is topic-aware or not, IM problem only focuses on the active nodes and overlooks all the inactive ones. Actually, an inactive node may receive the information from their active in-neighbors and become informed. Therefore, this type of nodes should also be considered when measuring the coverage of information propagation. Inspired by this, we formulate a new problem calledtopic-aware information coverage maximization(TAICM), which aims to maximize the sum of the expected number of both active and informed nodes in topic-aware social networks. Then we devise a heuristic method to solve it. Experiments on three real-world datasets demonstrate that our method can achieve similar or higher information coverage in much less or at least acceptable time than some commonly used IM algorithms.

The well-known influence maximization problem (IM) aims at maximizing the influence of one information cascade in a social network by selecting appropriate seed users prior to the diffusion process. In its adaptive version, additional seed users can be selected after observing certain diffusion results. On the other hand, social computing tasks are often time-critical, and therefore, only the influence resulted in the early period is worthwhile, which can be naturally modeled by enforcing a time constraint. In this article, we present an analysis of the time-constrained adaptive IM problem. On the theory side, we provide the hardness results of computing the optimal policy and a lower bound on the adaptive gap, which measures the superiority of adaptive policies over the nonadaptive policies. For practical solutions, from basic to advanced, we design a series of seeding policies for achieving high efficacy and scalability. Finally, we investigate the proposed solutions through extensive simulations based on real-world data sets.

In Online Social Networks (OSNs), privacy issue is a growing concern as more and more users are sharing their candid personal information and friendships online. One simple yet effective attack aims at private user data is to use socialbots to befriend the users and crawl data from users who accept the attackers' friend requests. With the attackers involving, individual users' preference and habit analysis is available, hence it is easier for the attackers to trick the users and befriend them. To better protect private information, some cautious, high-profile users may refer to their friends' decisions when receiving a friend request. The aim for this paper is to analyze the vulnerability of OSN users under this attack, in a more realistic setting that the high profile users having a different friend request acceptance model. Specifically, despite the existing probabilistic acceptance models, we introduce a deterministic linear threshold acceptance model for the cautious users such that they will only accept friend requests from users sharing at least a certain number of mutual friends with them. The model makes the cautious users harder to befriend with and complicates the attack. Although the new problem with multiple acceptance models is non-submodular and has no performance guarantee in general, we introduce the concept of adaptive submodular ratio and establish an approximation ratio under certain conditions. In addition, our results are also verified by extensive experiments in real-world OSN data sets.

Guiding users to actively expanding their online social circles is one of the primary strategies for enhancing user participation and growing online social networks. In this paper, we study the active friending problem which aims at providing users with the strategy for methodically sending invitations to successfully build a friendship with target users. We consider the prominent linear threshold model for the friending process and formulate the active friending problem as an optimization problem. The key observation is the relationship between the active friending problem and the minimum subset cover problem, based on which we present the first randomized algorithm with a data-independent approximation ratio and a controllable success probability for general graphs. The performance of the proposed algorithm is theoretically analyzed and supported by encouraging simulation results done on extensive datasets.

In this paper, we study the cost-aware target viral marketing (CTVM) problem, a generalization of influence maximization. CTVM asks for the most cost-effective users to influence the most relevant users. In contrast to the vast literature, we attempt to offer exact solutions. As the problem is NP-hard, thus, exact solutions are intractable, we propose TipTop, a$(1-\epsilon)$-optimal solution for arbitrary$\epsilon >0$that scales to very large networks, such as Twitter. At the heart of TipTop lies an innovative technique that reduces the number of samples as much as possible. This allows us to exactly solve CTVM on a much smaller space of generated samples using integer programming. Furthermore, TipTop lends a tool for researchers to benchmark their solutions against the optimal one in large-scale networks, which is currently not available.

Device-to-device (D2D) communication has recently gained much attention for its potential to boost the capacity of cellular systems. D2D enables direct communication between devices while bypassing a base station (BS), hence decreasing the load of BSs and increasing the network throughput via spatial reuse of radio resources. However, the cellular system is highly dynamic, an optimal allocation plan of radio resource to D2D links at one time point can easily become suboptimal when devices move. Thus, to maximize spatial reuse in cellular systems, it is crucial to update the resource allocation adaptively to reflect the current system status. In this paper, we develop the first adaptive solution framework to the dynamic resource problem for maximizing spatial reuse. At the core of the framework, we present the two algorithms for the adaptive set multicover problem with approximation ratio f and log n, respectively, where f is the frequency of the most frequent element and n is the total number of elements. The experimental results not only show that our solutions have a significant improvement in running time, compared with optimal or approximated offline methods, but also demonstrate their good performance through the resource usage, network throughput, and other metrics.

The vulnerability of interdependent networks has recently drawn much attention, especially in the key infrastructure networks such as power and communication networks. However, the existing works mainly considered a single cascade model across the networks and there is a need for more accurate models and analysis. In this paper, we focus on the interdependent power/communication networks to accurately analyze their vulnerability by considering heterogeneous cascade models. Accurately analyzing interdependent networks is challenging as the cascades are heterogeneous yet interdependent. Also, including multiple timescales into the context can further increase the complexity. To better depict the vulnerability of interdependent networks, we first propose a method to learn a threshold model from historical data to characterize the cascades in the power network and alleviate the need of calculating complicated power network dynamics. Next, we introduce message passing equations to generalize the threshold model in the power network and the percolation model in the communication network, based on which we derive efficient solution for finding the most critical nodes in the interdependent networks. Removing the most critical nodes can cause the largest cascade and thus characterizes the vulnerability. We evaluate the performance of the proposed methods in various datasets and discuss how network parameters, such as the timescales, can impact the vulnerability.

In this work, we examine the problem of adaptively uncovering network topology in an incomplete network, to support more accurate decision making in various real-world applications, such as modeling for reconnaissance attacks and network probing. While this problem has been partially studied, we provide a novel take on it by modeling it with a set of crawlers termed “bots” which can uncover independent portions of the network in parallel. Accordingly, we develop three adaptive algorithms, which make decisions based on previous observations due to incomplete information, namely AGP, a sequential method; FastAGP, a parallel algorithm; and ALSP, an extension of FastAGP uses local search to improve guarantees. These algorithms are proven to have 1/3, 1/7, and 1/ (5 + ϵ) approximation ratios, respectively. The key analysis of these algorithms is the connection between adaptive algorithms and an intersection of multiple partition matroids. We conclude with an evaluation of these algorithms to quantify the impact of both adaptivity and parallelism. We find that in practice, adaptive approaches perform significantly better, while FastAGP performs nearly as well as AGP in most cases despite operating in a massively parallel fashion. Finally, we show that a balance between the quantity and quality of bots is ideal for maximizing observation of the network.

Motivated by online social networks that are linked together through overlapping users, we study the influence maximization problem on a multiplex, with each layer endowed with its own model of influence diffusion. This problem is a novel version of the influence maximization problem that necessitates new analysis incorporating the type of propagation on each layer of the multiplex. We identify a new property, generalized deterministic submodular, which when satisfied by the propagation in each layer, ensures that the propagation on the multiplex overall is submodular-for this case, we formulate influential seed finder (ISF), the greedy algorithm with approximation ratio (1-1/e). Since the size of a multiplex comprising multiple OSNs may encompass billions of users, we formulate an algorithm knapsack seeding of network (KSN) that runs on each layer of the multiplex in parallel. KSN takes an α-approximation algorithm A for the influence maximization problem on a single network as input, and has approximation ratio ((1 - ϵ)α)/((o + 1)k) for arbitrary ϵ > 0, o is the number of overlapping users, and k is the number of layers in the multiplex. Experiments on real and synthesized multiplexes validate the efficacy of the proposed algorithms for the problem of influence maximization in the heterogeneous multiplex. Implementations of ISF and KSN are available at http://www.alankuhnle.com/papers/mim/mim.html.

In assessing privacy on online social networks, it is important to investigate their vulnerability to reconnaissance strategies, in which attackers lure targets into being their friends by exploiting the social graph in order to extract victims' sensitive information. As the network topology is only partially revealed after each successful friend request, attackers need to employ an adaptive strategy. Existing work only considered a simple strategy in which attackers sequentially acquire one friend at a time, which causes tremendous delay in waiting for responses before sending the next request, and which lack the ability to retry failed requests after the network has changed. In contrast, we investigate an adaptive and parallel strategy, of which attackers can simultaneously send multiple friend requests in batch and recover from failed requests by retrying after topology changes, thereby significantly reducing the time to reach the targets and greatly improving robustness. We cast this approach as an optimization problem, Max-Crawling, and show it inapproximable within (1 - 1/e + ε). We first design our core algorithm PM-AReST which has an approximation ratio of (1 - e-(1-1/e)) using adaptive monotonic submodular properties. We next tighten our algorithm to provide a nearoptimal solution, i.e. having a ratio of (1 - 1/e), via a two-stage stochastic programming approach. We further establish the gap bound of (1 - e-(1-1/e)2) between batch strategies versus the optimal sequential one. We experimentally validate our theoretical results, finding that our algorithm performs nearoptimally in practice and that this is robust under a variety of problem settings.

Online Social Networks (OSNs) are effective platforms for viral marketing. Due to their importance, viral marketing related problems in OSNs have been extensively studied in the past decade. However, none of the existing works can cope with the situation that the propagation rate dynamically increases for popular topics, as they all assume known propagation rates. In this paper, to better describe realistic information propagation in OSNs, we propose a novel model, Dynamic Influence Propagation (DIP), that allows propagation rate to change during the diffusion. We then define a new research problem: Threshold Activation Problem under DIP (TAP-DIP) to study the impact of DIP. TAP-DIP adds extra complexity on the already #P-hard TAP problem. Despite it hardness, we are able to approximate TAP-DIP with O(log|V|) ratio. Sitting in the core of our algorithm are the Lipschitz optimization technique and a novel solution to the general version of TAP, the Multi-TAP problem. Using various real OSN datasets, we experimentally demonstrate the impact of DIP and that our solution not only generates high-quality seed sets when being aware of the rate increase, but also is scalable.

One of the most central problems in viral marketing is Influence Maximization (IM), which finds a set of k seed users who can influence the maximum number of users in online social networks. Unfortunately, all existing algorithms to IM, including the state of the art SSA and IMM, have an approximation ratio of (1 - 1/e - ϵ). Recently, a generalization of IM, Cost-aware Target Viral Marketing (CTVM), asks for the most cost-effective users to influence the most relevant users, has been introduced. The current best algorithm for CTVM has an approximation ratio of (1 - 1/√e - ϵ). In this paper, we study the CTVM problem, aiming to optimally solve the problem. We first highlight that using a traditional two stage stochastic programming to exactly solve CTVM is not possible because of scalability. We then propose an almost exact algorithm TIPTOP, which has an approximation ratio of (1 - ϵ). This result significantly improves the current best solutions to both IM and CTVM. At the heart of TIPTOP lies an innovative technique that reduces the number of samples as much as possible. This allows us to exactly solve CTVM on a much smaller space of generated samples using Integer Programming. While obtaining an almost exact solution, TIPTOP is very scalable, running on billion-scale networks such as Twitter under three hours. Furthermore, TIPTOP lends a tool for researchers to benchmark their solutions against the optimal one in large-scale networks, which is currently not available.

A reconnaissance attack, in which attackers lure targets into becoming their friends in order to extract victims' sensitive information for sale or use in future attacks, is one of the most dangerous attacks in social networks. The core of this attack lies in intelligently sending friend requests to a small subset of users, called Critical Friending Set (CFS), so that the attacker can evade current defense mechanisms. Motivated by the above, we present a new paradigm to measure OSN vulnerability in light of reconnaissance attacks. Specifically, we introduce a new optimization problem, namely Min-Friending, which identifies a minimum CFS to friend in order to obtain at least Q benefit, in terms of personal information. A significant challenge of this problem is that network information (i.e. who friends with whom) is generally unknown to attackers. In this paper, we show that Min-Friending is inapproximable within a factor of (1 - o(1)) ln Q and present an adaptive algorithm which has a tight performance bound of (1+ln Q) using adaptive stochastic optimization. The key feature of our solution lies in the adaptive method, where partial network topology is revealed during each successful friend request. Thus the decision of sending each friend request is made taking into account observation about the outcomes of past decisions.

Opportunistic networks enable mobile devices to communicate with each other through routes that are built dynamically, while messages are en route between the sender and the destination(s). The social structure and interaction of users of such devices dictate the performance of routing protocols in those networks. Community structures, commonly exhibited by social networks, is also observed in the encounter patterns in opportunistic networks and has an astounding impact in designing forwarding algorithms for such types of networks. In this paper, we explore the structural vulnerability of social-based forwarding and routing methods in opportunistic networks. In particular, we introduce Community Vulnerability Assessment (CVA), a new problem on assessing the performance reliability of opportunistic routing strategies in Delay Tolerant Networks (DTN) from a community structure point of view. Given a positive number k, CVA aims to find out the k most vulnerable devices in the network whose non-participation (due to out-of-service or permanent out-of-range) transforms the current network community structure to a totally different one. As the first study in this direction, we analyze and provide key insights into the separation of network communities, evaluated via the Normalized Mutual Information (NMI). Based on these findings, we suggest an approximation algorithm for the special case when k 1/4 1, and a heuristic, genEdge, for the general case. To certify the effectiveness of our proposed approaches, we first test them on synthesized data with known community structures, and then we show the impact of node removal on community structures in real social networks. Finally we evaluate the performance via different forwarding and routing strategies in multiple real-world DTN traces. Our results indicate that, in many forwarding and routing methods, the nonparticipation of only some important devices is significant enough to degrade the entire network's performance.

Online social networks have become an effective and important social platform for communication, opinions exchange, and information sharing. However, they also make it possible for rapid and wide misinformation diffusion, which may lead to pernicious influences on individuals or society. Hence, it is extremely important and necessary to detect the misinformation propagation by placing monitors. In this article, we first define a general misinformation-detection problem for the case where the knowledge about misinformation sources is lacking, and show its equivalence to the influence-maximization problem in the reverse graph. Furthermore, considering node vulnerability, we aim to detect the misinformation reaching to a specific user. Therefore, we study a τ-Monitor Placement problem for cases where partial knowledge of misinformation sources is available and prove its # P complexity. We formulate a corresponding integer program, tackle exponential constraints, and propose a Minimum Monitor Set Construction (MMSC) algorithm, in which the cut-set 2 has been exploited in the estimation of reachability of node pairs. Moreover, we generalize the problem from a single target to multiple central nodes and propose another algorithm based on a Monte Carlo sampling technique. Extensive experiments on real-world networks show the effectiveness of proposed algorithms with respect to minimizing the number of monitors.

Many social networks and complex systems are found to be naturally divided into clusters of densely connected nodes, known as community structure (CS). Finding CS is one of fundamental yet challenging topics in network science. One of the most popular classes of methods for this problem is to maximize Newman's modularity. However, there is a little understood on how well we can approximate the maximum modularity as well as the implications of finding community structure with provable guarantees. In this paper, we settle definitely the approximability of modularity clustering, proving that approximating the problem within any (multiplicative) positive factor is intractable, unless P = NP. Yet we propose the first additive approximation algorithm for modularity clustering with a constant factor. Moreover, we provide a rigorous proof that a CS with modularity arbitrary close to maximum modularity QOPT might bear no similarity to the optimal CS of maximum modularity. Thus even when CS with near-optimal modularity are found, other verification methods are needed to confirm the significance of the structure.

Smart Grid addresses the problem of existing power grid's increasing complexity, growing demand and requirement for greater reliability, through two-way communication and automated residential load control among others. These features also makes the Smart Grid a target for a number of cyber attacks. In the paper, we study the problem of rate alteration attack (RAA) through fabrication of price messages which induces changes in load profiles of individual users and eventually causes major alteration in the load profile of the entire network. Combining with cascading failure, it ends up with a highly damaging attack. We prove that the problem is NP-Complete and provide its inapproximability. We devise two approaches for the problem, former deals with maximizing failure of lines with the given resource and then extending the effect with cascading failure while the later takes cascading potential into account while choosing the lines to fail. To get more insight into the impact of RAA, we also extend our algorithms to maximize number of node failures. Empirical results on both IEEE Bus data and real network help us evaluate our approaches under various settings of grid parameters.

Device-to-device (D2D) communications has recently emerged as a promising technology for boosting the capacity of cellular systems. D2D enables direct communication between mobile devices over the cellular band without utilizing infrastructure nodes such as base stations, thereby reducing the load on cellular base stations and increasing network throughput through spatial reuse of radio resources. Hence it is important to optimally allocate these radio resources. Furthermore, since the composition of a cellular macro cell is highly dynamic, it is critical to adaptively update the resource allocation for D2D communications rather than recomputing it from scratch. In this work, we develop the first online algorithm, namely ODSRA, for dynamic resource allocation while maximizing spatial reuse. At the core of the resource allocation problem is the online set multicover problem, for which we present the first deterministic O (log n log m)-competitive online algorithm, where n is the number of elements, and m the number of sets. By simulation, we show the efficacy of ODSRA by analyzing network throughput and other metrics, obtaining a large improvement in running time over offline methods.