KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

The structure and dynamics of multilayer networks

http://wpage.unina.it/valerio.persico/pubs/CloudIoT_FGCS.pdf

Integration of Cloud computing and Internet of Things

This article reviews the state-of-the-art in overlapping community detection algorithms, quality measures, and benchmarks. A thorough comparison of different algorithms (a total of fourteen) is provided. In addition to community-level evaluation, we propose a framework for evaluating algorithms' ability to detect overlapping nodes, which helps to assess overdetection and underdetection. After considering community-level detection performance measured by normalized mutual information, the Omega index, and node-level detection performance measured by F-score, we reached the following conclusions. For low overlapping density networks, SLPA, OSLOM, Game, and COPRA offer better performance than the other tested algorithms. For networks with high overlapping density and high overlapping diversity, both SLPA and Game provide relatively stable performance. However, test results also suggest that the detection in such networks is still not yet fully resolved. A common feature observed by various algorithms in real-world networks is the relatively small fraction of overlapping nodes (typically less than 30p), each of which belongs to only 2 or 3 communities.

Overlapping community detection in networks: The state-of-the-art and comparative study

What is Twitter

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.

/pdf/structural-vulnerability-analysis-of-overlapping-communities-2b7kkw4ugl.pdf

Structural Vulnerability Analysis of Overlapping Communities in Complex Networks

We introduce A$$^3$$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$$^3$$3CS in a wide range of dynamic networks.

/pdf/a-near-optimal-adaptive-algorithm-for-maximizing-modularity-1qpdjnwgqd.pdf

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

Pooling designs are used in DNA library screening to efficiently distinguish positive from negative clones, which is fundamental for studying gene functions and many other biological applications. One challenge is to design decoding algorithms for determining whether a clone is positive based on the test outcomes and a binary matrix representing the pools. This is more difficult in practice due to errors in biological experiments. More challenging still is a third category of clones called 'inhibitors' whose effect is to neutralise positives. We present a novel decoding algorithm identifying all positive clones in the presence of inhibitors and experimental errors.

/pdf/decoding-algorithms-in-pooling-designs-with-inhibitors-and-1jkzl47cjv.pdf

Decoding algorithms in pooling designs with inhibitors and error-tolerance

Embodiments of the invention provide systems and methods for deactivating reactive jamming attacks and other sophisticated attacks in wireless sensor networks (WSNs). In one system, trigger nodes (nodes whose transmissions invoke jammer nodes) are identified and communications between the sensor nodes of the WSN are routed to avoid sending (e.g., transmitting) information from identified trigger nodes. For example, identified trigger nodes are routed as receivers only. One method of identification uses an advanced randomized error-tolerant non-adaptive group testing technique and a clique-independent set problem solution. Another method of identification uses a hexagon tiling coloring and sequential group testing scheme.

Efficient protocols against sophisticated reactive jamming attacks

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.

My T. Thai

Papers

Structural Vulnerability Analysis of Overlapping Communities in Complex Networks

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

Decoding algorithms in pooling designs with inhibitors and error-tolerance

Efficient protocols against sophisticated reactive jamming attacks

Network Clustering via Maximizing Modularity: Approximation Algorithms and Theoretical Limits