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

Bilevel optimization has been applied to a wide variety of machine learning models, and numerous stochastic bilevel optimization algorithms have been developed in recent years. However, most existing algorithms restrict their focus on the single-machine setting so that they are incapable of handling the distributed data. To address this issue, under the setting where all participants compose a network and perform peer-to-peer communication in this network, we developed two novel decentralized stochastic bilevel optimization algorithms based on the gradient tracking communication mechanism and two different gradient estimators. Additionally, we established their convergence rates for nonconvex-strongly-convex problems with novel theoretical analysis strategies. To our knowledge, this is the first work achieving these theoretical results. Finally, we applied our algorithms to practical machine learning models, and the experimental results confirmed the efficacy of our algorithms.

On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network

—5G radio access network (RAN) with network slicing methodology plays a key role in the development of the next-generation network system. RAN slicing focuses on splitting the substrate’s resources into a set of self-contained programmable RAN slices . Leveraged by network function virtualization (NFV), a RAN slice is constituted by various virtual network functions (VNFs) and virtual links that are embedded as instances on sub- strate nodes. In this work, we focus on the following fundamental tasks: i) establishing the theoretical foundation for constructing a VNF mapping plan for RAN slice recovery optimization and ii) developing algorithms needed to map/embed VNFs efﬁciently. In particular, we propose four efﬁcient algorithms, including Resource-based Algorithm (RBA), Connectivity-based Algorithm (CBA), Group-based Algorithm (GBA), and Group-Connectivity-based Algorithm (GCBA) to solve the resource allocation and VNF mapping problem. Extensive experiments are also con-ducted to validate the robustness of RAN slicing via the proposed algorithms.

Optimizing Resource Allocation and VNF Embedding in RAN Slicing

The remarkable discovery of many large-scale real networks is the power-law distribution in degree sequence: the number of vertices with degree i is proportional to i −β for some constant β>1. A lot of researchers believe that it may be easier to solve some optimization problems in power-law graphs. Unfortunately, many problems have been proved NP-hard even in power-law graphs as Ferrante proposed in Ferrante et al. (Theoretical Computer Science 393(1–3):220–230, 2008). Intuitively, a theoretical question is raised: Are these problems on power-law graphs still as hard as on general graphs? The chapter shows that many optimal substructure problems, such as minimum dominating set, minimum vertex cover and maximum independent set, are easier to solve in power-law graphs by illustrating better inapproximability factors. An optimization problem has the property of optimal substructure if its optimal solution on some given graph is essentially the union of the optimal subsolutions on all maximal connected components. In particular, the above problems and a more general problem (ρ-minimum dominating set) are proven to remain APX-hard and their constant inapproximability factors on general power-law graphs by using the cycle-based embedding technique to embed any d-bounded graphs into a power-law graph. In addition, the corresponding inapproximability factors of these problems are further proven in simple power-law graphs based on the graphic embedding technique as well as that of maximum clique and minimum coloring using the embedding technique in Ferrante et al. (Theoretical Computer Science 393 (1–3):220–230, 2008). As a result of these inapproximability factors, the belief that there exists some (1+o(1))-approximation algorithm for these problems on power-law graphs is proven not always true. In addition, this chapter contains the in-depth investigations in the relationship between the exponential factor β and constant greedy approximation algorithms. The last part includes some minor NP-hardness results on simple power-law graphs for small β<1.

Hardness Complexity of Optimal Substructure Problems on Power-Law Graphs

Assessing network vulnerability is a central research topic to understand networks structures, thus providing an efficient way to protect them from attacks and other disruptive events. Existing vulnerability assessments mainly focus on investigating the inhomogeneous properties of graph elements, node degree, for example; however, these measures and the corresponding heuristic solutions cannot either provide an accurate evaluation over general network topologies

Hardness and Approximation of Network Vulnerability

Black box has been an important tool in studying computational complexity theory and has been used for establishing the hardness of problems. With an exponential growth in big data recently, data-driven computation has utilized black box as a tool for proving solutions to some computational problems. In this note, we present several observations on this new role of black box using reduction techniques in the computational complexity theory.

My T. Thai

Papers

On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network

Optimizing Resource Allocation and VNF Embedding in RAN Slicing

Hardness Complexity of Optimal Substructure Problems on Power-Law Graphs

Hardness and Approximation of Network Vulnerability

Black-Box and Data-Driven Computation