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Zhenxiang Gao

Bio: Zhenxiang Gao is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Centrality & Load balancing (computing). The author has an hindex of 3, co-authored 14 publications receiving 60 citations. Previous affiliations of Zhenxiang Gao include Beijing University of Posts and Telecommunications & University of Southern California.

Papers
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Journal ArticleDOI
TL;DR: This paper first uses the temporal evolution graph model, which can more accurately capture the topology dynamics of the mobile social network over time, to redefine three common centrality metrics: degree centrality, closeness centrality and betweenness centrality.
Abstract: Mobile social networks exploit human mobility and consequent device-to-device contact to opportunistically create data paths over time. While links in mobile social networks are time-varied and strongly impacted by human mobility, discovering influential nodes is one of the important issues for efficient information propagation in mobile social networks. Although traditional centrality definitions give metrics to identify the nodes with central positions in static binary networks, they cannot effectively identify the influential nodes for information propagation in mobile social networks. In this paper, we address the problems of discovering the influential nodes in mobile social networks. We first use the temporal evolution graph model which can more accurately capture the topology dynamics of the mobile social network over time. Based on the model, we explore human social relations and mobility patterns to redefine three common centrality metrics: degree centrality, closeness centrality and betweenness centrality. We then employ empirical traces to evaluate the benefits of the proposed centrality metrics, and discuss the predictability of nodes' global centrality ranking by nodes' local centrality ranking. Results demonstrate the efficiency of the proposed centrality metrics.

25 citations

Journal ArticleDOI
TL;DR: KG-Predict as discussed by the authors is a knowledge graph computational framework for drug repurposing, which integrates multiple types of entities and relations from various genotypic and phenotypic databases.

16 citations

Journal ArticleDOI
TL;DR: This paper proposes the semi locally evaluated centrality metric to identify influential nodes for message forwarding in opportunistic networks, and presents a simple message forwarding algorithm that demonstrates the efficiency of the proposed metric.
Abstract: —Opportunistic networks exploit human mobility and consequent device-to-device contacts to opportunistically create data paths over time. Identifying influential nodes as relay is a crucial problem for efficient routing in opportunistic networks. The degree centrality method is very simple but of little relevance. Although closeness centrality and betweenness centrality can effectively identify influential nodes, they are incapable to be applied in large-scale networks due to the high computational complexity. In this paper, we focus on designing an effective centrality ranking metric with low computational complexity in opportunistic networks. We propose the semi locally evaluated centrality metric to identify influential nodes for message forwarding in opportunistic networks. We also present a simple message forwarding algorithm, and employ real world mobility traces and synthetic mobility traces to evaluate the benefits of the proposed semi locally evaluated centrality metric. Results demonstrate the efficiency of the proposed metric in opportunistic networks.

7 citations

Proceedings ArticleDOI
01 Nov 2016
TL;DR: This paper proposes SSRS, a novel social-based secure routing strategy, which exploits social relationships to enhance routing security and performance and Simulations have been conducted on the real world data set and results demonstrate that SSRS achieves better performances than the existing algorithms.
Abstract: Mobile social networks (MSNs) are modern paradigms of delay tolerant networks, which exploit human mobility and consequent wireless contacts between mobile devices to share information in a peer-to-peer manner. Since routing in MSNs depends heavily on the cooperation among participating nodes, selfish or malicious behaviors of nodes impact strongly on the routing performance. In this paper, we introduce the social relationship evaluation method (SRM) for detecting the quality of human social relationship and propose the trustworthy behavior evaluation method (TBM) which exploits recommendations from close friends to detect node's selfish or malicious behaviors. Based on SRM, we define the community of each node as the set of nodes having close social relationships with this node either directly or indirectly, and define the local community centrality to identify influential nodes for message forwarding in the community. Then, we propose SSRS, a novel social-based secure routing strategy, which exploits social relationships to enhance routing security and performance. Simulations have been conducted on the real world data set and results demonstrate that SSRS achieves better performances than the existing algorithms.

4 citations


Cited by
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Journal Article
TL;DR: In this paper, the authors explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users and find that 93% potential predictability for user mobility across the whole user base.
Abstract: A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.

118 citations

Journal ArticleDOI
TL;DR: Simulation results show that the proposed community-based mediator (CbM), which considers the entropy of a random walk from a node to each community is proposed as a metrics performs better than the existing methods to spread information quickly and it can also introduce new influential nodes that other methods failed to identify.
Abstract: Applying effective methods to identify important nodes in a complex network is highly invaluable. Recently, in a complex network, finding a powerful leader of the community to spread information quickly throughout the network is the concern of many researchers. In this paper, to identify influential nodes in a large and complex network, community-based mediator (CbM), which considers the entropy of a random walk from a node to each community is proposed as a metrics. CbM describes how the node is essential to connect two or more than two communities of the network. Correlations between CbM and other classical methods used to identify influential nodes are discussed. The performance of CbM is evaluated by susceptible-infected-recovered (SIR) model. In SIR model, the node is the most powerful node in the network, if the percentage of infected node is more while the node is used as the source of infection. Simulation results show that the proposed method performs better than the existing methods to spread information quickly and it can also introduce new influential nodes that other methods failed to identify.

81 citations

Journal ArticleDOI
TL;DR: The results show that the exponential time-ordered aggregation method can measure TCNR centrality in a certain time interval more accurately than other aggregation methods, and the proposed time-ordering aggregation model-based centrality metric TCNR outperforms other existing temporal centrality metrics.
Abstract: How to measure the centrality of nodes is a significant problem in mobile social networks (MSNs). Current studies in MSNs mainly focus on measuring the centrality of nodes in a certain time interval based on the static graph that do not change over time. However, the network topology of MSNs is changing very rapidly, which is the main characteristic of MSNs. Therefore, it will not be accurate to measure the centrality of nodes in a certain time interval by using the static graph. To solve this problem, this paper first introduces a new centrality metric named cumulative neighboring relationship (CNR) for MSNs. Then, a time-ordered aggregation model is proposed to reduce a dynamic network to a series of time-ordered networks. Based on the time-ordered aggregation model, this paper proposes three particular time-ordered aggregation methods and combines with the proposed centrality metric CNR to measure the importance of nodes in a certain time interval. Finally, extensive trace-driven simulations are conducted to evaluate the performance of our proposed time-ordered aggregation model-based centrality metric time-ordered cumulative neighboring relationship ( TCNR ). The results show that the exponential time-ordered aggregation method can measure TCNR centrality in a certain time interval more accurately than other aggregation methods, and our proposed time-ordered aggregation model-based centrality metric TCNR outperforms other existing temporal centrality metrics.

36 citations

Journal ArticleDOI
Mengtian Li1, Ruisheng Zhang1, Rongjing Hu1, Fan Yang1, Yabing Yao1, Yongna Yuan1 
TL;DR: A novel centrality named clustered local-degree (CLD) is proposed, which combines the sum of the degrees of the nearest neighbors of a given node to rank spreaders, and shows that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes.
Abstract: Identifying influential spreaders is a crucial problem that can help authorities to control the spreading process in complex networks. Based on the classical degree centrality (DC), several improved measures have been presented. However, these measures cannot rank spreaders accurately. In this paper, we first calculate the sum of the degrees of the nearest neighbors of a given node, and based on the calculated sum, a novel centrality named clustered local-degree (CLD) is proposed, which combines the sum and the clustering coefficients of nodes to rank spreaders. By assuming that the spreading process in networks follows the susceptible–infectious–recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performances between the CLD centrality and other six measures. The results show that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes, and exposes the best performance to identify influential spreaders accurately.

30 citations

Journal ArticleDOI
TL;DR: This paper empirically investigate a new link prediction method base on similarity and compare nine well-known local similarity measures on nine real networks and proposed a new method to measure link predictability via local information and Shannon entropy.
Abstract: Predicting missing links is of both theoretical value and practical interest in network science. In this paper, we empirically investigate a new link prediction method base on similarity and compare nine well-known local similarity measures on nine real networks. Most of the previous studies focus on the accuracy, however, it is crucial to consider the link predictability as an initial property of networks itself. Hence, this paper has proposed a new link prediction approach called evidential measure (EM) based on Dempster–Shafer theory. Moreover, this paper proposed a new method to measure link predictability via local information and Shannon entropy.

25 citations