Truss decomposition in massive networks

Wang, Jia; Cheng, James

doi:10.14778/2311906.2311909

Cited by 323 publications

(231 citation statements)

References 28 publications

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“…For a fair comparison, we compare the k-influential community with the k + 1 truss community. This is because a k +1 truss is a k-core [24], and our k-influential community is based on k-core. Below, we consider the case when k = 4.…”

Section: Case Studiesmentioning

confidence: 99%

Influential community search in large networks

et al. 2015

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Community search is a problem of finding densely connected subgraphs that satisfy the query conditions in a network, which has attracted much attention in recent years. However, all the previous studies on community search do not consider the influence of a community. In this paper, we introduce a novel community model called k-influential community based on the concept of k-core, which can capture the influence of a community. Based on the new community model, we propose a linear-time online search algorithm to find the top-r k-influential communities in a network. To further speed up the influential community search algorithm, we devise a linear-space index structure which supports efficient search of the top-r k-influential communities in optimal time. We also propose an efficient algorithm to maintain the index when the network is frequently updated. We conduct extensive experiments on 7 real-world large networks, and the results demonstrate the efficiency and effectiveness of the proposed methods.

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Section: Case Studiesmentioning

confidence: 99%

Influential community search in large networks

et al. 2015

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“…2: for each vertex v in G do 3: set v.min id edge to be the edge on v that has the minimum index. 4: Create a tree node n(e) for each edge e. 5: for i = 1 to n do 6: Assume ei has two vertices v1 and v2, create an array: min neighbors[2] = {v1.min id edge, v2.min id edge}; 7: for each edge em in min neighbors do 8: if m < i and currently n(ei) and n(em) are not in the same subtree then 9: connect n(ei) to root(n(em)) //n(ei) is parent K-Core [5] and K-Truss [16], [17](also called Triangle KCore in [4], DN-graph in [3] ) are two common dense subgraph patterns that draw much attention in recent works. The definitions of K-Core and K-Truss are as follows:…”

Section: Algorithm 3 Constructing Edge Scalar Treementioning

confidence: 99%

Analyzing and Visualizing Scalar Fields on Graphs

Zhang

Wang

Parthasarathy

2017

2017 IEEE 33rd International Conference on Data Engineering (ICDE)

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Abstract-The value proposition of a dataset often resides in the implicit interconnections or explicit relationships (patterns) among individual entities, and is often modeled as a graph. Effective visualization of such graphs can lead to key insights uncovering such value. In this article we propose a visualization method to explore graphs with numerical attributes associated with nodes (or edges) -referred to as scalar graphs. Such numerical attributes can represent raw content information, similarities, or derived information reflecting important network measures such as triangle density and centrality. The proposed visualization strategy seeks to simultaneously uncover the relationship between attribute values and graph topology, and relies on transforming the network to generate a terrain map. A key objective here is to ensure that the terrain map reveals the overall distribution of components-of-interest (e.g. dense subgraphs, k-cores) and the relationships among them while being sensitive to the attribute values over the graph. We also design extensions that can capture the relationship across multiple numerical attributes (scalars). We demonstrate the efficacy of our method on several real-world data science tasks while scaling to large graphs with millions of nodes.

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“…The k-plex is still NP-Complete since it restricts the subgraph size, while k-core further relaxes it to achieve the linear time complexity with respect to the number of edges. A new direction based on the edge triangle model, like DN-Graph [24] and truss decomposition [22], is more suitable for social network analysis since it captures the tie strength between actors inside the subgroup. Our proposed mutual friend concept belongs to this model and we will compare it with the above two concepts in Section 3 in details.…”

Section: Related Workmentioning

confidence: 99%

“…Truss decomposition is a process to compute the k-truss of a graph G for all 2 ≤ k ≤ kmax, in which k-truss is a cohesive subgraph ensures that all the edges in it are supported by at least (k − 2) triangles [22]. The truss definition is similar to but proposed independently with the mutual friend defined in this paper except the meaning for k. Besides, the authors for truss decomposition realize that memory solution can not handle large scale social networks.…”

Section: Comparison To Truss Decompositionmentioning

confidence: 99%

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Large scale cohesive subgraphs discovery for social network visual analysis

Zhao

Tung

2012

Proc. VLDB Endow.

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Graphs are widely used in large scale social network analysis nowadays. Not only analysts need to focus on cohesive subgraphs to study patterns among social actors, but also normal users are interested in discovering what happening in their neighborhood. However, effectively storing large scale social network and efficiently identifying cohesive subgraphs is challenging. In this work we introduce a novel subgraph concept to capture the cohesion in social interactions, and propose an I/O efficient approach to discover cohesive subgraphs.Besides, we propose an analytic system which allows users to perform intuitive, visual browsing on large scale social networks. Our system stores the network as a social graph in the graph database, retrieves a local cohesive subgraph based on the input keywords, and then hierarchically visualizes the subgraph out on orbital layout, in which more important social actors are located in the center. By summarizing textual interactions between social actors as tag cloud, we provide a way to quickly locate active social communities and their interactions in a unified view.

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Truss decomposition in massive networks

Cited by 323 publications

References 28 publications

Influential community search in large networks

Influential community search in large networks

Analyzing and Visualizing Scalar Fields on Graphs

Large scale cohesive subgraphs discovery for social network visual analysis

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