Real Social Graphs Research Articles

Privacy Protection for Marginal-Sensitive Community Individuals Against Adversarial Community Detection Attacks

Social networks especially the social communities facilitate the rapid and rich social activities of all individuals in the world. However, advanced community detection brings serious privacy disclosure (e.g., whether two important and sensitive individuals belong to the same community?) to us. For instance, online plainclothes policemen can be regarded as marginal community users who are often in the same community initially and in need of penetrating into as many as possible different communities to collect illegal evidence of network criminals, but adversarial community inference which can maliciously disclose the sensitive user relationships within a target community will expose their privacy and lead to task failure. Thus, privacy protection for the marginal community users becomes an urgent issue which is still open so far. In this work, we aim to study the community privacy protection for target marginal individuals of a community against multiple adversarial community detection (ACD) attacks. First, we define the marginal community user hiding problem and propose a marginal user pair selection strategy. Second, to enhance the privacy effectiveness of conventional methods, we propose a deep graph learning approach to maximally find the minimum link perturbation cost. Finally, we conduct various community detection attacks on many real social graphs, and the experimental results show that our method can more effectively hide the marginal-sensitive user pairs than baselines.

Adversarial link deception against the link prediction in complex networks

Currently, the link prediction tool has been extensively used in kinds of complex networks for the use of friend, commodity, or service recommendations. However, many adversaries may maliciously or intentionally perturb a part of social links to deceive the link prediction method to suggest some unexpected missing links (referred to as targets) to users. In this work, from the attacker perspective, we propose to promote the prediction probability of given targets via adding a tiny number of new links into the network to deceive the common neighbor based link prediction method. We first define the link deception process as a similarity score maximizing problem. Secondly, we propose to use a greedy algorithm referred to as GreedyAdd to greedily adding a budget limited number of links into the network. Thirdly, considering the high time complexity of the GreedyAdd, we propose a heuristic link addition method referred to as HeuristicAdd to improve the computing efficiency. Finally, we do experiments on many real social graphs to confirm the effectiveness and efficiency of the HeuristicAdd method. The results show that the HeuristicAdd algorithm can mostly deceive the link prediction with less time consumptions than the GreedyAdd. This work considers the security problem of complex systems from a new perspective and has potential applications.

A Distributed Hybrid Community Detection Methodology for Social Networks

Nowadays, the amount of digitally available information has tremendously grown, with real-world data graphs outreaching the millions or even billions of vertices. Hence, community detection, where groups of vertices are formed according to a well-defined similarity measure, has never been more essential affecting a vast range of scientific fields such as bio-informatics, sociology, discrete mathematics, nonlinear dynamics, digital marketing, and computer science. Even if an impressive amount of research has yet been published to tackle this NP-hard class problem, the existing methods and algorithms have virtually been proven inefficient and severely unscalable. In this regard, the purpose of this manuscript is to combine the network topology properties expressed by the loose similarity and the local edge betweenness, which is a currently proposed Girvan–Newman’s edge betweenness measure alternative, along with the intrinsic user content information, in order to introduce a novel and highly distributed hybrid community detection methodology. The proposed approach has been thoroughly tested on various real social graphs, roundly compared to other classic divisive community detection algorithms that serve as baselines and practically proven exceptionally scalable, highly efficient, and adequately accurate in terms of revealing the subjacent network hierarchy.

Metadata-conscious anonymous messaging

Anonymous messaging platforms allow users to spread messages over a network (e.g., a social network) without revealing message authorship to other users. Popular demand for anonymous messaging is evidenced by the success of mobile apps like Whisper and Yik Yak. In such platforms, the spread of messages is typically modeled as a diffusion process. Recent advances in network analysis have revealed that such diffusion processes are vulnerable to author deanonymization by adversaries with access to metadata, such as timing information. In this work, we ask the fundamental question of how to intervene in the propagation of anonymous messages in order to make it difficult to find the source. In particular, we study the performance of a message propagation protocol called adaptive diffusion introduced in [1] . We prove that it achieves asymptotically optimal source-hiding and significantly outperforms standard diffusion. We further demonstrate empirically that adaptive diffusion hides the source effectively on real social graphs.

On maximizing diffusion speed in social networks

A variety of models have been proposed and analyzed to understand how a new innovation (e.g., a technology, a product, or even a behavior) diffuses over a social network, broadly classified into either of epidemic-based or game-based ones. In this paper, we consider a game-based model, where each individual makes a selfish, rational choice in terms of its payoff in adopting the new innovation, but with some noise. We study how diffusion effect can be maximized by seeding a subset of individuals (within a given budget), i.e., convincing them to pre-adopt a new innovation. In particular, we aim at finding `good' seeds for minimizing the time to infect all others, i.e., diffusion speed maximization . To this end, we design polynomial-time approximation algorithms for three representative classes, Erdőos-Réenyi, planted partition and geometrically structured graph models, which correspond to globally well-connected, locally well-connected with large clusters and locally well-connected with small clusters, respectively, provide their performance guarantee in terms of approximation and complexity. First, for the dense Erdős-Rényi and planted partition graphs, we show that an arbitrary seeding and a simple seeding proportional to the size of clusters are almost optimal with high probability. Second, for geometrically structured sparse graphs, including planar and d -dimensional graphs, our algorithm that (a) constructs clusters, (b) seeds the border individuals among clusters, and (c) greedily seeds inside each cluster always outputs an almost optimal solution. We validate our theoretical findings with extensive simulations under a real social graph. We believe that our results provide new practical insights on how to seed over a social network depending on its connection structure, where individuals rationally adopt a new innovation. To our best knowledge, we are the first to study such diffusion speed maximization on the game-based diffusion, while the extensive research efforts have been made in epidemic-based models, often referred to as influence maximization .

Dynamic Communities and their Detection

Overlapping community detection has already become an interesting problem in data mining and also a useful technique in applications. This underlines the importance of following the lifetime of communities in real graphs. Palla et al. developed a promising method, and analyzed community evolution on two large databases [23]. We have followed their footsteps in analyzing large real-world databases and found, that the framework they use to describe the dynamics of communities is insufficient for our data. The method used by Palla et al. is also dependent on a very special community detection algorithm, the clique percolation method, and on its monotonic nature. In this paper we propose an extension of the basic community events described in [23] and a method capable of handling communities found a nonmonotonic community detection algorithm. We also report on findings that came from the tests on real social graphs.