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

We consider the Threshold Activation Problem (TAP): given social network G and positive threshold T, find a minimum-size seed set A that can trigger expected activation of at least T. We introduce the first scalable, parallelizable algorithm with performance guarantee for TAP suitable for datasets with millions of nodes and edges; we exploit the bicriteria nature of solutions to TAP to allow the user to control the running time versus accuracy of our algorithm through a parameter α ∊ (0, 1): given η > 0, with probability 1 − η our algorithm returns a solution A with expected activation greater than T — 2αΤ, and the size of the solution A is within factor 1−h 4αΤ + log(T) of the optimal size. The algorithm runs in time O (α−2 log (n/η) (n + m)|A|), where n, m, refer to the number of nodes, edges in the network. The performance guarantee holds for the general triggering model of internal influence and also incorporates external influence, provided a certain condition is met on the cost-effectivity of seed selection.

Scalable bicriteria algorithms for the threshold activation problem in online social networks

Since no fixed infrastructure and no centralized management present in wireless networks, a connected dominating set (CDS) of the graph representing the network is widely used as a virtual backbone. Constructing a minimum CDS is NP-hard. In this paper, we propose a new greedy algorithm, called S-MIS, with the help of Steiner tree that can construct a CDS within a factor of 4.8 + ln5 from the optimal solution. We also introduce the distributed version of this algorithm. We prove that the proposed algorithm is better than the current best performance ratio which is 6.8. A simulation is conducted to compare S-MIS with its variation which is rS-MIS. The simulation shows that the sizes of the CDSs generated by S-MIS and rS-MIS are almost the same. Copyright © 2005 John Wiley & Sons, Ltd.

On greedy construction of connected dominating sets in wireless networks: Research Articles

Complex Social Networks is a newly emerging (hot) topic with applications in a variety of domains, such as communication networks, engineering networks, social networks, and biological networks. In the last decade, there has been an explosive growth of research on complex real-world networks, a theme that is becoming pervasive in many disciplines, ranging from mathematics and computer science to the social and biological sciences. Optimization of complex communication networks requires a deep understanding of the interplay between the dynamics of the physical network and the information dynamics within the network. Although there are a few books addressing social networks or complex networks, none of them has specially focused on the optimization perspective of studying these networks. This book provides the basic theory of complex networks with several new mathematical approaches and optimization techniques to design and analyze dynamic complex networks. A wide range of applications and optimization problems derived from research areas such as cellular and molecular chemistry, operations research, brain physiology, epidemiology, and ecology.

Handbook of Optimization in Complex Networks: Communication and Social Networks

Due to high complexity of many modern machine learning models such as deep convolutional networks, understanding the cause of model's prediction is critical. Many explainers have been designed to give us more insights on the decision of complex classifiers. However, there is no common ground on evaluating the quality of different classification methods. Motivated by the needs for comprehensive evaluation, we introduce the c-Eval metric and the corresponding framework to quantify the explainer's quality on feature-based explainers of machine learning image classifiers. Given a prediction and the corresponding explanation on that prediction, c-Eval is the minimum-power perturbation that successfully alters the prediction while keeping the explanation's features unchanged. We also provide theoretical analysis linking the proposed parameter with the portion of predicted object covered by the explanation. Using a heuristic approach, we introduce the c-Eval plot, which not only displays a strong connection between c-Eval and explainers' quality, but also serves as a low-complexity approach of assessing explainers. We finally conduct extensive experiments of explainers on three different datasets in order to support the adoption of c-Eval in evaluating explainers' performance.

Evaluating Explainers via Perturbation

Region coverage and network connectivity are among the most important problems for the quality of service in wireless sensor networks. Unfortunately, due to the sensor failures and hostile environments, such as active volcanic regions or battle fields, the emergence of coverage holes and disconnections among sensors is unavoidable. One way to handle this problem is to deploy mobile sensors in the network, which is called hybrid sensor networks, so that these mobile sensors can be relocated to heal the holes or maintain the network connectivity. However, because of the low-power of mobile sensors, it is extremely challenging to design a fast and effective movement schedule for mobile sensors to (1) maintain both the region coverage and network connectivity at any time, and (2) minimize the moving energy consumption. In this paper, we develop an adaptive algorithm, AHCH algorithm, to adaptively heal the holes with the guarantee of network connectivity without recomputing from scratch. By comparing AHCH algorithm with the optimal solution at each time-slot, we show its expected adaptive approximation ratio as O(log |M|) with |M| mobile sensors in some special cases. In more general cases, we extend our AHCH algorithm to InAHCH and GenAHCH algorithms, handling insufficient mobile sensors as well as disconnected regions, along with the proof of their corresponding theoretical adaptive approximation ratios. The experimental evaluation shows the effectiveness of our proposed algorithms with respect to both low energy consumption and hole healing latency.

My T. Thai

Papers

Scalable bicriteria algorithms for the threshold activation problem in online social networks

On greedy construction of connected dominating sets in wireless networks: Research Articles

Handbook of Optimization in Complex Networks: Communication and Social Networks

Evaluating Explainers via Perturbation

Adaptive approximation algorithms for hole healing in hybrid wireless sensor networks