Current graph neural network (GNN) model-stealing methods rely heavily on queries to the victim model, assuming no hard query limits. However, in reality, the number of allowed queries can be severely limited. In this paper, we demonstrate how an adversary can extract a GNN with very limited interactions with the model. Our approach first enables the adversary to obtain the model backbone without making direct queries to the victim model and then to strategically utilize a fixed query limit to extract the most informative data. The experiments on eight real-world datasets demonstrate the effectiveness of the attack, even under a very restricted query limit and under defense against model extraction in place. Our findings underscore the need for robust defenses against GNN model extraction threats.

This study tackles the challenge of neutralizing a malicious drone swarm targeting critical infrastructure. Given the impracticality of destroying all drones, we propose an uncertainty-aware threat assessment method, modeling payload size and positional uncertainties probabilistically. We studynode removalas the core decision problem to fragment communication and reduce attack power. The threat of each drone is quantified by its expected payload and distance-based probabilistic model. We also assess the probabilistic connectivity between drones to evaluate the swarm’s robustness. A novel evaluation function integrates these factors, and we introduce a probabilistic greedy search algorithm to minimize the swarm’s threat. We evaluate against centrality-based and weight-augmented dismantling heuristics, a recent DQN policy, and a small-n brute-force oracle. Our approach achieves a 70% reduction in attack power and near-optimal performance, deviating by less than 5% from the best possible outcomes under uncertainty.

Signed graphs serve as fundamental data structures for representing positive and negative relationships in social networks, with signed graph neural networks (SGNNs) emerging as the primary tool for their analysis. Our investigation reveals that balance theory, while essential for modeling signed relationships in SGNNs, inadvertently introduces exploitable vulnerabilities to black-box attacks. To showcase this, we propose balance-attack, a novel adversarial strategy specifically designed to compromise graph balance degree, and develop an efficient heuristic algorithm to solve the associated NP-hard optimization problem. While existing approaches attempt to restore attacked graphs through balance learning techniques, they face a critical challenge we term “Irreversibility of Balance-related Information,” as restored edges fail to align with original attack targets. To address this limitation, we introduce Balance Augmented-Signed Graph Contrastive Learning (BA-SGCL), an innovative framework that combines contrastive learning with balance augmentation techniques to achieve robust graph representations. By maintaining high balance degree in the latent space, BA-SGCL not only effectively circumvents the irreversibility challenge but also significantly enhances model resilience. Extensive experiments across multiple SGNN architectures and real-world datasets demonstrate both the effectiveness of our proposed balance-attack and the superior robustness of BA-SGCL, advancing the security and reliability of signed graph analysis in social networks. Datasets and codes of the proposed framework are at the github repositoryhttps://github.com/JialongZhou666/BA-SGCL.git.

The pervasive dissemination of fake news presents significant challenges to societal well-being and informed decision-making, necessitating robust detection mechanisms with calibrated uncertainty measures. This paper proposes a novel hybrid framework for fake news detection, integrating uncertainty quantification with a domain-specific Knowledge Base approach. The BANED knowledge base models word-level probabilistic significance, leveraging statistical support metrics to assess prediction uncertainty. By incorporating these metrics into a Bayesian framework, our method provides well-calibrated predictive distributions, offering enhanced interpretability and robustness in the presence of ambiguous or conflicting news data. The proposed approach is evaluated on the FakeNewsNet and ISOT Fake News datasets, demonstrating competitive accuracy and superior reliability compared to state-of-the-art Bayesian inference techniques. Combining word-level probabilistic significance with Monte Carlo Dropout decreases mean calibration error and narrows the interquartile range of predictions. Full code and supplementary materials of BANED might be found at https://github.com/micbizon/BANED.

This paper targets a new foundation for designing general-purpose learning systems, by establishing a consensus method that facilitates self-adaptation and flexibility to deal with different learning tasks and different data distribution. We present the Shapely Consensus Deep Learning (SCDL) as a consensus method for general-purpose solutions that do not require the help of domain experts. SCDL is two-level based learning process. In the first level, several deep learning models are trained and the Shapley Value is used to determine the contribution of each subset of models in the training. The models are pruned according to their contribution in the learning process. In the second level, the loss information of each data distribution is saved in the knowledge base. Both levels are explored to prune the models for each new observation. We present the evaluation of the generality of SCDL using different datasets with different shapes, and complexities. The results reveal the effectiveness of SCDL for weakly classification. Concretely, SCDL achieved 90% of AUC with less than 86% for the baseline solutions.

As the metaverse grows in popularity and complexity, securing its virtual environment is critical. Metaverse intrusion detection involves identifying and preventing unauthorized access, malicious activities, and potential threats. To address these challenges, we propose a novel Metaverse intrusion detection system (MIDS) that combines generative adversarial networks (GAN) and Transformer-based classifiers. The system operates in three stages: 1) generating diverse and realistic network traffic using GAN; 2) detecting intrusions with a Transformer-based classifier; and 3) ensuring data privacy through federated learning and a trusted authority mechanism. Unlike traditional methods, our approach employs dual aggregation, generating both global and local models tailored to users’ needs. Tested on public datasets, the method achieves state-of-the-art performance with an F1-score of 0.9984, demonstrating its effectiveness in generating realistic training data and improving MIDS performance. This approach can extend to other security domains requiring diverse data for training.

We present a novel approach for addressing computer vision tasks in intelligent transportation systems, with a strong focus on data security during training through federated learning. Our method leverages visual transformers, training multiple models for each image. By calculating and storing visual image features as well as loss values, we propose a novel Shapley value model based on model performance consistency to select the most appropriate models during testing. To enhance security, we introduce an intelligent federated learning strategy, where users are grouped into clusters based on constrastive clustering for creating a global model as well as customized local models. Users receive both global as well as local models, enabling tailored computer vision applications. We evaluated KGVT-ITS (Knowledge Guided Visual Transformers for Intelligent Transportation Systems) on various ITS challenges, including pedestrian detection, abnormal event detection, as well as near-crash detection. The results demonstrate the superiority of KGVT-ITS over baseline solutions, showcasing its effectiveness and robustness in intelligent transportation scenarios. More particularly, KGVT-ITS achieves significant improvements of about 8% against the existing ITS methods.

Graph Neural Networks (GNNs) are recognized as potent tools for processing real-world data organized in graph structures. Especially inductive GNNs, which allow for the processing of graph-structured data without relying on predefined graph structures, are becoming increasingly important in a wide range of applications. As such these networks become attractive targets for model-stealing attacks where an adversary seeks to replicate the functionality of the targeted network. Significant efforts have been devoted to developing model-stealing attacks that extract models trained on images and texts. However, little attention has been given to stealing GNNs trained on graph data. This paper identifies a new method of performing unsupervised model-stealing attacks against inductive GNNs, utilizing graph contrastive learning and spectral graph augmentations to efficiently extract information from the targeted model. The new type of attack is thoroughly evaluated on six datasets and the results show that our approach outperforms the current state-of-the-art by Shen et al. (2021). In particular, our attack surpasses the baseline across all benchmarks, attaining superior fidelity and downstream accuracy of the stolen model while necessitating fewer queries directed toward the target model.

Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry. However, existing graph anomaly detection algorithms focus on distinguishing individual entities (nodes or graphs) and overlook the possibility of anomalous groups within the graph. To address this limitation, this paper introduces a novel unsupervised framework for a new task called Group-level Graph Anomaly Detection (Gr-GAD). The proposed framework first employs a variant of Graph AutoEncoder (GAE) to locate anchor nodes that belong to potential anomaly groups by capturing long-range inconsistencies. Subsequently, group sampling is employed to sample candidate groups, which are then fed into the proposed Topology Pattern-based Graph Contrastive Learning (TPGCL) method. TPGCL utilizes the topology patterns of groups as clues to generate embeddings for each candidate group and thus distinct anomaly groups. The experimental results on both real-world and synthetic datasets demonstrate that the proposed framework shows superior performance in identifying and localizing anomaly groups, highlighting it as a promising solution for Gr-GAD. Datasets and codes of the proposed framework are at the github repository https://github.com/STiL-Team/Topology-Pattern-Enhanced-Unsupervised-Group-level-Graph-Anomaly-Detection.git.

Safeguarding critical infrastructure has recently emerged as a global challenge. To address complex security concerns raised by broadening array of threats, effective mobile security forces are essential. A key aspect involves designing optimal patrolling strategies for mobile units. Two bodies of research dealt with this: stochastic patrolling and partially observable stochastic games. Alas, the first approach makes too-far-reaching simplifying assumption and the second one is more expressive but computationally challenging. The model proposed in this paper is inspired by partially observable stochastic games so that it is general enough to enable comprehensive modeling of attacker-defender interactions but a the same time remains computationally friendly. With our proposed robust SHIELD algorithm, we are able to find a defense strategy where the probability of apprehending the attacker can be nearly doubled compared to the state of the art.

We introduce an innovative methodology for the identification of vehicular collisions within Internet of Vehicles (IoV) applications. This approach combines a knowledge base system with deep learning for model selection in an ensemble learning setting. It is designed to provide a general near-crash detection capability without relying on domain-specific knowledge, enabling the development of generic deep learning models. Our proposed methodology employs a novel deep learning approach, wherein multiple learning models are individually trained for each image. Subsequently, visual features are computed and stored for each trained image, along with the associated loss values from the training phase. This stored information is utilized to select the most suitable models for processing new image data during the testing phase. To facilitate efficient model selection, we employ a kNN (k Nearest Neighbors) strategy. To enhance both data and model security in IoV environments, we implement an intelligent federated learning (FL) strategy. Users are organized into clusters, and we employ two distinct aggregation methods, departing from conventional federated learning approaches. In the initial stage, we aggregate model data from all users to create a global model representing collective knowledge. In the subsequent stage, we aggregate models from each cluster to generate customized local models. Users are provided with both global and local models, allowing them to select the most suitable model for their specific crash detection needs. We test our approach, that we call Knowledge Guided Deep Learning for Near Crash Detection (KGDL-NCD), on well-known NCD benchmarks. The results demonstrate that KGDL-NCD surpasses baseline solutions, achieving an AUC (Area Under Curve) metric of 0.95.

We present a novel approach to detecting fake news in Internet of Things (IoT) applications. By investigating federated learning and trusted authority methods, we address the issue of data security during training. Simultaneously, by investigating convolution transformers and user clustering, we deal with multi-modality in fake news data. Firstly, we use dense embedding and the k-means algorithm to cluster users into groups that are similar to one another. We then develop a local model for each user using their local data. The server then receives the local models of the users along with the clustering information, and a trusted authority verifies their integrity there. We use two different types of aggregation in place of conventional federated learning systems. The initial step is to combine all the users' models to create a single global model. The second step entails compiling each user's model into a local model of comparable users. Both models are supplied to the users, who then select the most suitable model for identifying fake news. By conducting extensive experiments using Twitter data, we demonstrate that the proposed method outperforms various baselines, where it achieves an average accuracy of 0.85 in comparison to others that do not exceed 0.81.

Random Walks-based Anomaly Detection (RWAD) is commonly used to identify anomalous patterns in various applications. An intriguing characteristic of RWAD is that the input graph can either be pre-existing graphs or feature-derived graphs constructed from raw features. Consequently, there are two potential attack surfaces against RWAD: graph-space attacks and feature-space attacks. In this paper, we explore this vulnerability by designing practical coupled-space (interdependent feature-space and graph-space) attacks, investigating the interplay between graph-space and feature-space attacks. To this end, we conduct a thorough complexity analysis, proving that attacking RWAD is NP-hard. Then, we proceed to formulate the graph-space attack as a bi-level optimization problem and propose two strategies to solve it: alternative iteration (alterI-attack) or utilizing the closed-form solution of the random walk model (cf-attack). Finally, we utilize the results from the graph-space attacks as guidance to design more powerful feature-space attacks (i.e., graph-guided attacks). Comprehensive experiments demonstrate that our proposed attacks are effective in enabling the target nodes to evade the detection from RWAD with a limited attack budget. In addition, we conduct transfer attack experiments in a black-box setting, which show that our feature attack significantly decreases the anomaly scores of target nodes. Our study opens the door to studying the coupled-space attack against graph anomaly detection in which the graph space relies on the feature space.

Signed social networks are widely used to model the trust relationships among online users in security-sensitive systems such as cryptocurrency trading platforms, where trust prediction plays a critical role. In this paper, we investigate how attackers could mislead trust prediction by secretly manipulating signed networks. To this end, we first design effective poisoning attacks against representative trust prediction models. The attacks are formulated as hard bi-level optimization problems, for which we propose several efficient approximation solutions. However, the resultingbasic attackswould severely change the structural semantics (in particular, both local and global balance properties) of a signed network, which makes the attacks prone to be detected by the powerful attack detectors we designed. Given this, we further refine the basic attacks by integrating someconflicting metricsas penalty terms into the objective function. Therefined attacksbecome secrecy-aware, i.e., they can successfully evade attack detectors with high probability while sacrificing little attack performance. We conduct comprehensive experiments to demonstrate that the basic attacks can severely disrupt trust prediction but could be easily detected, and the refined attacks perform almost equally well while evading detection. Overall, our results significantly advance the knowledge in designing more practical attacks, reflecting more realistic threats to current trust prediction models. Moreover, the results also provide valuable insights and guidance for building up robust trust prediction systems.

A standard model of a security game assumes a one-off assault during which the attacker cannot update their strategy even if new actionable insights are gained in the process. In this paper, we propose a version of a security game that takes into account a possibility of a two-phase attack. Specifically, in the first phase, the attacker makes a preliminary move to gain extra information about this particular instance of the game. Based on this information, the attacker chooses an optimal concluding move. We derive a compact-form mixed-integer linear program that computes an optimal strategy of the defender. Our simulation shows that this strategy mitigates serious losses incurred to the defender by a two-phase attack while still protecting well against less sophisticated attackers.

Centrality measures can rank nodes in a social network according to their importance. However, in many cases, a node may want to avoid being highly ranked by such measures, e.g., as is the case with terrorist networks. In this work, we study a confrontation between the seeker—the party analyzing a social network using centrality measures—and the evader—a node attempting to decrease its ranking according to such measures. We analyze the possible outcomes of modifying, i.e., adding or removing, a single edge by the evader, showing that even without complete knowledge about the network, the effects of the modification on the evader's ranking can often be predicted. We study the computational complexity of finding a set of modifications that reduce the evader's centrality ranking in an optimal way, proving that these decision problems are NP-complete. Moreover, we provide a 2-approximation for the degree centrality, and logarithmic approximation boundaries for the closeness and betweenness centralities. Finally, we define and investigate a Stackelberg game between the seeker and the evader, providing a Mixed Integer Linear Programming formulation of finding an equilibrium. Altogether, we provide a thorough analysis of the strategic aspects of hiding from centrality measures in social networks.

The Shapley value – a fundamental game-theoretic solution concept – has recently become one of the main tools used to explain predictions of tree ensemble models. Another well-known game-theoretic solution concept is the Banzhaf value. Although the Banzhaf value is closely related to the Shapley value, its properties w.r.t. feature attribution have not been understood equally well. This paper shows that, for tree ensemble models, the Banzhaf value offers some crucial advantages over the Shapley value while providing similar feature attributions. In particular, we first give an optimal O(TL + n) time algorithm for computing the Banzhaf value-based attribution of a tree ensemble model’s output. Here, T is the number of trees, L is the maximum number of leaves in a tree, and n is the number of features. In comparison, the state-of-the-art Shapley value-based algorithm runs in O(TLD^2 + n) time, where D denotes the maximum depth of a tree in the ensemble. Next, we experimentally compare the Banzhaf and Shapley values for tree ensemble models. Both methods deliver essentially the same average importance scores for the studied datasets using two different tree ensemble models (the sklearn implementation of Decision Trees or xgboost implementation of Gradient Boosting Decision Trees). However, our results indicate that, on top of being computable faster, the Banzhaf is more numerically robust than the Shapley value.

We extend the well-known model of graph-restricted games due to Myerson to signed graphs. In our model, it is possible to explicitly define not only that some players are friends (as in Myerson's model) but also that some other players are enemies. As such our games can express a wider range of situations, e.g., animosities between political parties. We define the value for signed graph games using the axiomatic approach that closely follows the celebrated characterization of the Myerson value. Furthermore, we propose an algorithm for computing an arbitrary semivalue, including the extension of the Myerson value proposed by us. We also develop a pseudo-polynomial algorithm for power indices in weighted voting games for signed graphs with bounded treewidth. Moreover, we consider signed graph games with a priori defined alliances (unions) between players and propose algorithms to compute the extension of the Owen value to this setting.

This article introduces a new model to identify a group of trajectory outliers from a large trajectory database and proposes several algorithms. These can be split into three categories: 1) algorithms based on data mining and knowledge discovery, which study the different correlations among the trajectory data and identify the group of abnormal trajectories from the knowledge extracted; 2) algorithms based on machine learning and computational intelligence methods, which use the ensemble learning and metaheuristics to find the group of trajectory outliers; and 3) an algorithm exploring the convolution deep neural network that learns the different features of historical data to determine the group of trajectory outliers. Experiments on different trajectory databases have been carried out to investigate the proposed algorithms. The results show that the deep learning solution outperforms data mining, machine learning, and computational intelligence solutions, as well as state-of-the-art solutions in terms of runtime and accuracy performance.

Centrality measures are the most commonly advocated social network analysis tools for identifying leaders of covert organizations. While the literature has predominantly focused on studying the effectiveness of existing centrality measures or developing new ones, we study the problem from the opposite perspective, by focusing on how a group of leaders can avoid being identified by centrality measures as key members of a covert network. More specifically, we analyze the problem of choosing a set of edges to be added to a network to decrease the leaders’ ranking according to three fundamental centrality measures, namely, degree, closeness, and betweenness. We prove that this problem is NP-complete for each measure. Moreover, we study how the leaders can construct a network from scratch, designed specifically to keep them hidden from centrality measures. We identify a network structure that not only guarantees to hide the leaders to a certain extent but also allows them to spread their influence across the network.

In this paper, we present a computational analysis of the problem of attacking sign prediction, whereby the aim of the attacker (a network member) is to hide from the defender (an analyst) the signs of a target set of links by removing the signs of some other, non-target, links. The problem turns out to be NP-hard if either local or global similarity measures are used for sign prediction. We propose a heuristic algorithm and test its effectiveness on several real-life and synthetic datasets.

Multilayer networks allow for modeling complex relationships, where individuals are embedded in multiple social networks at the same time. Given the ubiquity of such relationships, these networks have been increasingly gaining attention in the literature. This paper presents the first analysis of the robustness of centrality measures against strategic manipulation in multilayer networks. More specifically, we consider an “evader” who strategically chooses which connections to form in a multilayer network in order to obtain a low centrality-based ranking—thereby reducing the chance of being highlighted as a key figure in the network—while ensuring that she remains connected to a certain group of people. We prove that determining an optimal way to “hide” is NP-complete and hard to approximate for most centrality measures considered in our study. Moreover, we empirically evaluate a number of heuristics that the evader can use. Our results suggest that the centrality measures that are functions of the entire network topology are more robust to such a strategic evader than their counterparts which consider each layer separately.

Security games model the confrontation between a defender protecting a set of targets and an attacker who tries to capture them. A variant of these games assumes security interdependence between targets, facilitating contagion of an attack. So far, only stochastic spread of an attack has been considered. In this work, we introduce a version of security games, where the attacker strategically drives the entire spread of attack and where interconnections between nodes affect their susceptibility to be captured. We find that the strategies effective in the settings without contagion or with stochastic contagion are no longer feasible when spread of attack is strategic. While in the former settings it was possible to efficiently find optimal strategies of the attacker, doing so in the latter setting turns out to be an NP-complete problem for an arbitrary network. However, for some simpler network structures, such as cliques, stars, and trees, we show that it is possible to efficiently find optimal strategies of both players. For arbitrary networks, we study and compare the efficiency of various heuristic strategies. As opposed to previous works with no or stochastic contagion, we find that centrality-based defense is often effective when spread of attack is strategic, particularly for centrality measures based on the Shapley value.

Link prediction is one of the fundamental problems in social network analysis. A common set of techniques for link prediction rely on similarity metrics which use the topology of the observed subnetwork to quantify the likelihood of unobserved links. Recently, similarity metrics for link prediction have been shown to be vulnerable to attacks whereby observations about the network are adversarially modified to hide target links. We propose a novel approach for increasing robustness of similarity-based link prediction by endowing the analyst with a restricted set of reliable queries which accurately measure the existence of queried links. The analyst aims to robustly predict a collection of possible links by optimally allocating the reliable queries. We formalize the analyst's problem as a Bayesian Stackelberg game in which they first choose the reliable queries, followed by an adversary who deletes a subset of links among the remaining (unreliable) queries by the analyst. The analyst in our model is uncertain about the particular target link the adversary attempts to hide, whereas the adversary has full information about the analyst and the network. Focusing on similarity metrics using only local information, we show that the problem is NP-Hard for both players, and devise two principled and efficient approaches for solving it approximately. Extensive experiments with real and synthetic networks demonstrate the effectiveness of our approach.

The intuitive notion of added value in groups represents a fundamental property of biological, physical, and economic systems: how the interaction or cooperation of multiple entities, substances, or other agents can produce synergistic effects. However, despite the ubiquity of group formation, a well-founded measure of added value has remained elusive. Here, we propose such a measure inspired by the Shapley value —a fundamental solution concept from Cooperative Game Theory. To this end, we start by developing a solution concept that measures the average impact of each player in a coalitional game and show how this measure uniquely satisfies a set of intuitive properties. Then, building upon our solution concept, we propose a measure of added value that not only analyzes the interactions of players inside their group, but also outside it, thereby reflecting otherwise-hidden information about how these individuals typically perform in various groups of the population.

At the heart of multi-agent systems is the ability to cooperate to improve the performance of individual agents and/or the system as a whole. While a widespread assumption in the literature is that such cooperation is essentially unrestricted, in many realistic settings this assumption does not hold. A highly influential approach for modelling such scenarios are graph-restricted games introduced by Myerson [36]. In this approach, agents are represented by nodes in a graph, edges represent communication channels, and a group can generate an arbitrary value only if there exists a direct or indirect communication channel between every pair of agents within the group. Two fundamental solution-concepts that were proposed for such games are the Myerson value and the Shapley value . While an algorithm has been developed to compute the Shapley value in arbitrary graph-restricted games, no such general-purpose algorithm has been developed for the Myerson value to date. With this in mind, we set out to develop for such games a general-purpose algorithm to compute the Myerson value, and a more efficient algorithm to compute the Shapley value. Since the computation of either value involves enumerating all connected induced subgraphs of the game’s underlying graph, we start by developing an algorithm dedicated to this enumeration, and then we show empirically that it is faster than the state of the art in the literature. Finally, we present a sample application of both algorithms, in which we test the Myerson value and the Shapley value as advanced measures of node centrality in networks.

Assessing the progress made in AI and contributions to the state of the art is of major concern to the community. Recently, Frechette et al. [2016] advocated performing such analysis via the Shapley value, a concept from coalitional game theory. In this paper, we argue that while this general idea is sound, it unfairly penalizes older algorithms that advanced the state of the art when introduced, but were then outperformed by modern counterparts. Driven by this observation, we introduce the temporal Shapley value, a measure that addresses this problem while maintaining the desirable properties of the (classical) Shapley value. We use the tempo- ral Shapley value to analyze the progress made in (i) the different versions of the Quicksort algorithm; (ii) the annual SAT competitions 2007–2014; (iii) an annual competition of Constraint Programming, namely the MiniZinc challenge 2014–2016. Our analysis reveals novel insights into the development made in these important areas of research over time.

One of the fundamental research challenges in network science is centrality analysis, i.e., identifying the nodes that play the most important roles in the network. In this article, we focus on the game-theoretic approach to centrality analysis. While various centrality indices have been recently proposed based on this approach, it is still unknown how general is the game-theoretic approach to centrality and what distinguishes some game-theoretic centralities from others. In this article, we attempt to answer this question by providing the first axiomatic characterization of game-theoretic centralities. Specifically, we show that every possible centrality measure can be obtained following the game-theoretic approach. Furthermore, we study three natural classes of game-theoretic centrality, and prove that they can be characterized by certain intuitive properties pertaining to the well-known notion of Fairness due to Myerson.

Some game-theoretic solution concepts such as the Shapley value and the Banzhaf index have recently gained popularity as measures of node centrality in networks. While this direction of research is promising, the computational problems that surround it are challenging and have largely been left open. To date there are only a few positive results in the literature, which show that some game-theoretic extensions of degree-, closeness- and betweenness-centrality measures are computable in polynomial time, i.e., without the need to enumerate the exponential number of all possible coalitions. In this article, we show that these results can be extended to a much larger class of centrality measures that are based on a family of solution concepts known as semivalues. The family of semivalues includes, among others, the Shapley value and the Banzhaf index. To this end, we present a generic framework for defining game-theoretic network centralities and prove that all centrality measures that can be expressed in this framework are computable in polynomial time. Using our framework, we present a number of new and polynomial-time computable game-theoretic centrality measures.

How can individuals and communities protect their privacy against social network analysis tools? How do criminals or terrorists organizations evade detection by such tools? Under which conditions can these tools be made strategy proof? These fundamental questions have attracted little attention in the literature to date, as most social network analysis tools are built around the assumption that individuals or groups in a network do not act strategically to evade such tools. With this in mind, we outline in this paper a new paradigm for social network analysis, whereby the strategic behaviour of network actors is explicitly modeled. Addressing this research challenge has various implications. For instance, it may allow two individuals to keep their relationship secret or private. It may also allow members of an activist group to conceal their membership, or even conceal the existence of their group from authoritarian regimes. Furthermore, it may assist security agencies and counter terrorism units in understanding the strategies that covert organizations use to escape detection, and give rise to new strategy-proof countermeasures.

One of the fundamental research challenges in network science is the centrality analysis, i.e., identifying the nodes that play the most important roles in the network. In this paper, we focus on the game-theoretic approach to centrality analysis. While various centrality indices have been proposed based on this approach, it is still unknown what distinguishes this family of indices from the more classical ones. In this paper, we answer this question by providing the first axiomatic characterization of game-theoretic centralities. Specifically, we show that every centrality can be obtained following the game-theoretic approach, and show that two natural classes of game-theoretic centrality can be characterized by two intuitive properties pertaining to Myerson's notion of Fairness.

The dollar auction is an auction model used to analyse the dynamics of conflict escalation. In this paper, we analyse the course of an auction when participating players are spiteful, i.e., they are motivated not only by their own profit, but also by the desire to hurt the opponent. We investigate this model for the complete information setting, both for the standard scenario and for the situation where auction starts with non-zero bids. Our results give us insight into the possible effects of meanness onto conflict escalation.

Algorithms for NP-complete problems often have different strengths andweaknesses, and thus algorithm portfolios often outperform individualalgorithms. It is surprisingly difficult to quantify a component algorithm's contributionto such a portfolio. Reporting a component's standalone performance wronglyrewards near-clones while penalizing algorithms that have small but distinctareas of strength. Measuring a component's marginal contribution to an existingportfolio is better, but penalizes sets of strongly correlated algorithms,thereby obscuring situations in which it is essential to have at least onealgorithm from such a set. This paper argues for analyzing component algorithmcontributions via a measure drawn from coalitional game theory---the Shapleyvalue---and yields insight into a research community's progress over time. Weconclude with an application of the analysis we advocate to SAT competitions,yielding novel insights into the behaviour of algorithm portfolios, theircomponents, and the state of SAT solving technology.

Certain real-life networks have a community structure in which communities overlap. For example, a typical bus network includes bus stops (nodes), which belong to one or more bus lines (communities) that often overlap. Clearly, it is important to take this information into account when measuring the centrality of a bus stop - how important it is to the functioning of the network. For example, if a certain stop becomes inaccessible, the impact will depend in part on the bus lines that visit it. However, existing centrality measures do not take such information into account. Our aim is to bridge this gap. We begin by developing a new game-theoretic solution concept, which we call the Configuration semivalue, in order to have greater flexibility in modelling the community structure compared to previous solution concepts from cooperative game theory. We then use the new concept as a building block to construct the first extension of Closeness centrality to networks with community structure (overlapping or otherwise). Despite the computational complexity inherited from the Configuration semivalue, we show that the corresponding extension of Closeness centrality can be computed in polynomial time. We empirically evaluate this measure and our algorithm that computes it by analysing the Warsaw public transportation network.

The coalition structure generation problem is one of the key challenges in multi-agent coalition formation. It involves partitioning a set of agents into coalitions so that system performance is optimized. To date, the multi-agent systems literature has focused exclusively on the utilitarian version of this problem which seeks to maximize the sum of the values of the coalitions involved. However, there are many examples of situations in which other performance metrics are of interest. In particular, in games with non-transferable utility, we may be more interested in an egalitarian optimal coalition structure, or in minimizing the difference between the utilities of the most affluent and poorest agents. In this paper, we present a number of exact algorithms to solve such non-utilitarian formulations of the coalition structure generation problem.

Link prediction is a key problem in social network analysis: it involves making suggestions about where to add new links in a network, based solely on the structure of the network. We address a special case of this problem, whereby the new links are supposed to connect different communities in the network; we call it the interlinks prediction problem. This is particularly challenging as there are typically very few links between different communities. To solve this problem, we propose a local node-similarity measure, inspired by the Owen-value interaction index—a concept developed in cooperative game theory and fuzzy systems. Although this index requires an exponential number of operations in the general case, we show that our local node-similarity measure is computable in polynomial time. We apply our measure to solve the inter-links prediction problem in a number of real-life networks, and show that it outperforms all other local similarity measures in the literature.

Betweenness centrality measures the ability of different nodes to control the flow of information in a network. In this article, we extend the standard definition of betweenness centrality using Semivalues—a family of solution concepts from cooperative game theory that includes, among others, the Shapley value and the Banzhaf power index. Any Semivalue-based betweenness centrality measure (such as, for example, the Shapley value-based betweenness centrality measure) has the advantage of evaluating the importance of individual nodes by considering the roles they each play in different groups of nodes. Our key result is the development of a general polynomial-time algorithm to compute the Semivalue-based betweenness centrality measure, and an even faster algorithm to compute the Shapley value-based betweenness centrality measure, both for weighted and unweighted networks. Interestingly, for the unweighted case, our algorithm for computing the Shapley value-based centrality has the same complexity as the best known algorithm for computing the standard betweenness centrality due to Brandes [15]. We empirically evaluate our measures in a simulated scenario where nodes fail simultaneously. We show that, compared to the standard measure, the ranking obtained by our measures reflects more accurately the influence that different nodes have on the functionality of the network.

We propose a novel representation for coalitional games with externalities, called Partition Decision Trees. This representation is based on rooted directed trees, where non-leaf nodes are labelled with agents' names, leaf nodes are labelled with payoff vectors, and edges indicate membership of agents in coalitions. We show that this representation is fully expressive, and for certain classes of games significantly more concise than an extensive representation. Most importantly, Partition Decision Trees are the first formalism in the literature under which most of the direct extensions of the Shapley value to games with externalities can be computed in polynomial time.

Solution concepts from cooperative game theory, such as the Shapley value or the Banzhaf index, have recently been advocated as interesting extensions of standard measures of node centrality in networks. While this direction of research is promising, the computation of game-theoretic centrality can be challenging. In an attempt to address the computational issues of game-theoretic network centrality, we present a generic framework for constructing game-theoretic network centralities. We prove that all extensions that can be expressed in this framework are computable in polynomial time. Using our framework, we present the first game-theoretic extensions of weighted and normalized degree centralities, impact factor centrality,distance-scaled and normalized betweenness centrality,and closeness and normalized closeness centralities.

The Shapley value has been recently advocated as a method to choose the seed nodes for the process of information diffusion. Intuitively, since the Shapley value evaluates the average marginal contribution of a player to the coalitional game, it can be used in the network context to evaluate the marginal contribution of a node in the process of information diffusion given various groups of already 'infected' nodes. Although the above direction of research seems promising, the current liter- ature is missing a throughout assessment of its performance. The aim of this work is to provide such an assessment of the existing Shapley value-based approaches to information diffusion.

Many multiagent settings require a collection of agents to partition themselves into coalitions. In such cases, the agents may have conflicting preferences over the possible coalition structures that may form. We investigate a noncooperative bargaining game to allow the agents to resolve such conflicts and partition themselves into non-overlapping coalitions. The game has a finite horizon and is played over discrete time periods. The bargaining agenda is defined exogenously. An important element of the game is a parameter 0≤δ≤1 that represents the probability that bargaining ends in a given round. Thus, δ is a measure of the degree of democracy (ranging from democracy for δ=0, through increasing levels of authoritarianism as δ approaches 1, to dictatorship for δ=1). For this game, we focus on the question of how a player's position on the agenda affects his power. We also analyse the relation between the distribution of the power of individual players, the level of democracy, and the welfare efficiency of the game. Surprisingly, we find that purely democratic games are welfare inefficient due to an uneven distribution of power among the individual players. Interestingly, introducing a degree of authoritarianism into the game makes the distribution of power more equitable and maximizes welfare.

Inspired by emerging applications of social networks, we introduce in this paper a new centrality measure termed gate-keeper centrality. The new centrality is based on the well-known game-theoretic concept of Shapley value and, as we demonstrate, possesses unique qualities compared to the existing metrics. Furthermore, we present a dedicated approximate algorithm, based on the Monte Carlo sampling method, to compute the gatekeeper centrality. We also consider two well known applications in social network analysis, namely community detection and limiting the spread of mis-information; and show the merit of using the proposed framework to solve these two problems in comparison with the respective benchmark algorithms.

There is currently much interest in the problem of measuring the centrality of nodes in networks/graphs; such measures have a range of applications, from social network analysis, to chemistry and biology. In this paper we propose the first measure of node centrality that takes into account the community structure of the underlying network. Our measure builds upon the recent literature on game-theoretic centralities, where solution concepts from cooperative game theory are used to reason about importance of nodes in the network. To allow for flexible modelling of community structures, we propose a generalization of the Owen value—a well-known solution concept from cooperative game theory to study games with a priori-given unions of players. As a result we obtain the first measure of centrality that accounts for both the value of an individual node's relationships within the network and the quality of the community this node belongs to.

The Shapley value---probably the most important normative payoff division scheme in coalitional games---has recently been advocated as a useful measure of centrality in networks. However, although this approach has a variety of real-world applications (including social and organisational networks, biological networks and communication networks), its computational properties have not been widely studied. To date, the only practicable approach to compute Shapley value-based centrality has been via Monte Carlo simulations which are computationally expensive and not guaranteed to give an exact answer. Against this background, this paper presents the first study of the computational aspects of the Shapley value for network centralities. Specifically, we develop exact analytical formulae for Shapley value-based centrality in both weighted and unweighted networks and develop efficient (polynomial time) and exact algorithms based on them. We empirically evaluate these algorithms on two real-life examples (an infrastructure network representing the topology of the Western States Power Grid and a collaboration network from the field of astrophysics) and demonstrate that they deliver significant speedups over the Monte Carlo approach. For instance, in the case of unweighted networks our algorithms are able to return the exact solution about 1600 times faster than the Monte Carlo approximation, even if we allow for a generous 10% error margin for the latter method.

The current state-of-the-art algorithm for optimal coalition structure generation is IDP-IP — an algorithm that combines IDP (a dynamic programming algorithm due to Rahwan and Jennings, AAAI'08) with IP (a tree-search algorithm due to Rahwan et al., JAIR'09). In this paper we analyse IDP-IP, highlight its limitations, and then develop a new approach for combining IDP with IP that overcomes these limitations.

The conventional model of coalition formation considers every possible subset of agents as a potential coalition. However, in many real-world applications, there are inherent constraints on feasible coalitions: for instance, certain agents may be prohibited from being in the same coalition, or the coalition structure may be required to consist of coalitions of the same size. In this paper, we present the first systematic study of constrained coalition formation (CCF). We propose a general framework for this problem, and identify an important class of CCF settings, where the constraints specify which groups of agents should/should not work together. We describe a procedure that transforms such constraints into a structured input that allows coalition formation algorithms to identify, without any redundant computations, all the feasible coalitions. We then use this procedure to develop an algorithm for generating an optimal (welfare-maximizing) constrained coalition structure, and show that it outperforms existing state-of-the-art approaches by several orders of magnitude.

Coalition formation is a fundamental research topic in multi-agent systems. In this context, while it is desirable to generate a coalition structure that maximizes the sum of the values of the coalitions, the space of possible solutions is often too large to allow exhaustive search. Thus, a fundamental open question in this area is the following: Can we search through only a subset of coalition structures, and be guaranteed to find a solution that is within a desirable bound (beta) from optimum? If so, what is the minimum such subset? To date, the above question has only been partially answered by Sandholm et al. in their seminal work on anytime coalition structure generation [Sandholm et al., 1999]. More specifically, they identified minimum subsets to be searched for two particular bounds: β = n and β = [n/2]. Nevertheless, the question remained open for other values of β. In this paper, we provide the complete answer to this question.

In this paper we propose a novel approach to represent coalitional games, called a Coalition-Flow Network (CF-NET), that builds upon a generalization of the network flow literature. Specifically, this representation is based on our observation that the coalition formation process can be viewed as the problem of directing the flow through a network where every edge has certain capacity constraints.

We consider the issue of representing coalitional games in multi-agent systems with externalities (i.e., in systems where the performance of one coalition may be affected by other co-existing coalitions). In addition to the conventional partition function game representation (PFG), we propose a number of new representations based on a new notion of externalities. In contrast to conventional game theory, our new concept is not related to the process by which the coalitions are formed, but rather to the effect that each coalition may have on the entire system and vice versa. We show that the new representations are fully expressive and, for many classes of games, more concise than the conventional PFG. Building upon these new representations, we propose a number of approaches to solve the coalition structure generation problem in systems with externalities. We show that, if externalities are characterised by various degrees of regularity, the new representations allow us to adapt coalition structure generation algorithms that were originally designed for domains with no externalities, so that they can be used when externalities are present. Finally, building upon Rahwan et al. [16] and Michalak et al. [9], we present a unified method to solve the coalition structure generation problem in any system, with or without externalities, provided sufficient information is available.

The authors are grateful for financial support received from the UK EP-SRC through the project Market-Based Control of Complex Computational Systems (GR/T10657/01). The authors are also thankful to Jennifer McManus, School of English, University of Liverpool for excellent editorial assistance.