Abdul Alim

Structural Vulnerability Analysis of Overlapping Communities in Complex Networks

Many complex networks commonly exhibit community structure in their underlying organizations, i.e., They contain multiple groups of nodes having more connections inside a group and less interactions among groups. This special structure not only offers key insights into understanding the network organization principles but also plays a vital role in maintaining the normal function of the whole system. As a result, any significant change to the network communities, due to element-wise failures, can potentially redefine their organizational structures and consequently lead to the malfunction or undesirable corruption of the entire system. Therefore, identifying network elements that are essential to its community structure is a fundamental and important problem. However, to the best of our knowledge, this research direction has not received much been attention in the literature. In this paper, we study the structural vulnerability of overlapping complex network communities to identify nodes that are important in maintaining the complex structure organization. Specifically, given a network and a budget of k nodes, we want to identify k critical nodes whose their exclusions transforms the current network community structure. To effectively analyze this vulnerability on overlapping communities, we propose the concept of generating edges and provide an optimal algorithm for detecting the Minimal Generating Edge Set (MGES) in a network community. We suggest genEdge, an effective solution based on this MGES. Empirical results on both synthesized networks with known community structures, and real data including Reality cellular data, Foursquare and Facebook social traces confirm the efficacy of our approach.

Assessing network vulnerability in a community structure point of view

We introduce Community structure Vulnerability Assessment (CVA) problem to assess the network vulnerability under a community structure point of view. Given a positive number k, CVA aims to find out the k most vulnerable nodes whose removals maximally transform the current network community structure to a different one. As the first attempt, we suggest an approximation algorithm for the special case k = 1, and propose multiple greedy algorithms for CVA problem. To certify the effectiveness of suggested approaches, we test them on not only synthesized networks with known community structures but also on real-world social traces.

Misinformation in Online Social Networks: Detect Them All with a Limited Budget

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-set2 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.

A near-optimal adaptive algorithm for maximizing modularity in dynamic scale-free networks

We introduce A

Leveraging Social Communities for Optimizing Cellular Device-to-Device Communications

^3

Monitor placement to timely detect misinformation in Online Social Networks

3CS, an adaptive framework with approximation guarantees for quickly identifying community structure in dynamic networks via maximizing Modularity Q. Our framework explores the advantages of the power-law distribution property found in many real-world complex systems. The framework is scalable for very large networks, and more excitingly, possesses approximation factors to ensure the quality of its detected community structure. To the best of our knowledge, this is the first framework that achieves approximation guarantees for the NP-hard Modularity maximization problem, especially on dynamic scale-free networks. To certify our approach, we conduct extensive experiments in comparison with other adaptive methods on both synthesized networks with known community structures and real-world traces including ArXiv e-print citation and Facebook social networks. Excellent empirical results not only confirm our theoretical results but also promise the practical applicability of A

Are communities as strong as we think

^3