Finding k most influential edges on flow graphs

Wong, Petrie; Sun, Cliz; Lo, Eric; Yiu, Man Lung; Wu, Xiaowei; Zhao, Zhichao; Chan, T.-H. Hubert; Kao, Ben

doi:10.1016/j.is.2016.12.002

Cited by 14 publications

(3 citation statements)

References 31 publications

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“…In other words, they assume that edges connecting two connected components are important. There are also other studies that use flow/reachability 15 , 16 , bridgeness 19 , neighbors 17 , and clique degrees 18 to measure the edge importance.…”

Section: Introductionmentioning

confidence: 99%

Identifying critical higher-order interactions in complex networks

Aktaş

Nguyen

Jawaid

et al. 2021

Sci Rep

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Diffusion on networks is an important concept in network science observed in many situations such as information spreading and rumor controlling in social networks, disease contagion between individuals, and cascading failures in power grids. The critical interactions in networks play critical roles in diffusion and primarily affect network structure and functions. While interactions can occur between two nodes as pairwise interactions, i.e., edges, they can also occur between three or more nodes, which are described as higher-order interactions. This report presents a novel method to identify critical higher-order interactions in complex networks. We propose two new Laplacians to generalize standard graph centrality measures for higher-order interactions. We then compare the performances of the generalized centrality measures using the size of giant component and the Susceptible-Infected-Recovered (SIR) simulation model to show the effectiveness of using higher-order interactions. We further compare them with the first-order interactions (i.e., edges). Experimental results suggest that higher-order interactions play more critical roles than edges based on both the size of giant component and SIR, and the proposed methods are promising in identifying critical higher-order interactions.

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Section: Introductionmentioning

confidence: 99%

Identifying critical higher-order interactions in complex networks

Aktaş

Nguyen

Jawaid

et al. 2021

Sci Rep

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“…Betweenness centrality of edges 15 , 16 and betweenness centrality of a group of edges 17 suppose that edges linking two connected components are important. Average node reachability and the maximum flow of a network can characterize the ability of information transmission in networks and critical edges have serious influence on average node reachability and maximum flow 18 , 19 . In Jaccard coefficient 20 , if node i and node j have a lot of common neighbors, even if they have no direct connection, information also can spread from node i to node j easily, so edges are more important if there are less common neighbors.…”

Section: Introductionmentioning

confidence: 99%

Identifying critical edges in complex networks

Chen

Zhao³

2018

Sci Rep

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The critical edges in complex networks are extraordinary edges which play more significant role than other edges on the structure and function of networks. The research on identifying critical edges in complex networks has attracted much attention because of its theoretical significance as well as wide range of applications. Considering the topological structure of networks and the ability to disseminate information, an edge ranking algorithm BCCMOD based on cliques and paths in networks is proposed in this report. The effectiveness of the proposed method is evaluated by SIR model, susceptibility index S and the size of giant component σ and compared with well-known existing metrics such as Jaccard coefficient, Bridgeness index, Betweenness centrality and Reachability index in nine real networks. Experimental results show that the proposed method outperforms these well-known methods in identifying critical edges both in network connectivity and spreading dynamic.

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“…Most methods for graph sparsification focus on retaining important edges among the existing ones, such as [19] [1] [2]. There are many ways to measure the importance of edges.…”

mentioning

confidence: 99%

Graph Sparsification with Generative Adversarial Network

2020

Preprint

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Graph sparsification aims to reduce the number of edges of a network while maintaining its accuracy for given tasks. In this study, we propose a novel method called GSGAN, which is able to sparsify networks for community detection tasks. GSGAN is able to capture those relationships that are not shown in the original graph but are relatively important, and creating artificial edges to reflect these relationships and further increase the effectiveness of the community detection task. We adopt GAN as the learning model and guide the generator to produce random walks that are able to capture the structure of a network. Specifically, during the training phase, in addition to judging the authenticity of the random walk, discriminator also considers the relationship between nodes at the same time. We design a reward function to guide the generator creating random walks that contain useful hidden relation information. These random walks are then combined to form a new social network that is efficient and effective for community detection. Experiments with real-world networks demonstrate that the proposed GSGAN is much more effective than the baselines, and GSGAN can be applied and helpful to various clustering algorithms of community detection.

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Finding k most influential edges on flow graphs

Cited by 14 publications

References 31 publications

Identifying critical higher-order interactions in complex networks

Identifying critical higher-order interactions in complex networks

Identifying critical edges in complex networks

Graph Sparsification with Generative Adversarial Network

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