Scalable Diversified Ranking on Large Graphs

Li, Ronghua; Yu, Jeffery Xu

doi:10.1109/icdm.2011.126

Cited by 19 publications

(27 citation statements)

References 10 publications

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“…After the seminal work by Fagin et al [10,11], a large number of studies on top-k query processing have been done for different application scenarios, such as processing distributed preference queries [4], keyword queries [17], set similarity join queries [25]. Recently, many studies take the diversity into consideration in top-k query processing, in order to return diversified ranking results [26,18,1,16,2,27]. A comprehensive survey of top-k query processing can be found in [12].…”

Section: Related Workmentioning

confidence: 99%

“…After computing score(v * ), the algorithm uses the same process to update the set S by v * as the degree algorithm does (lines [16][17][18][19].…”

Section: Top-k Search Frameworkmentioning

confidence: 99%

“…For a popped component rooted by x, the algorithm iteratively examines the vertices in the component in increasing order of their degree (lines [14][15][16][17][18][19][20][21][22][23][24][25][26]. For such a vertex u, we will access its adjacency list N (u) to find out those vertices denoted as w that are also in N (v).…”

Section: : Return Count-components (G[v] T);mentioning

confidence: 99%

“…Then the algorithm iteratively finds the top-k results (lines [6][7][8][9][10][11][12][13][14][15][16][17][18][19] If…”

Section: Top-k Search Frameworkmentioning

confidence: 99%

“…According to Property 2, we also union v and w into one set in g [u], and union v and u into one set in g[w] (lines [11][12][13][14][15]. If u does not union with any other vertex, the algorithm invokes an Isolate operation on u to mark that u forms a size-1 component in g [v] (lines [16][17][18]. Symmetrically, by Property 1, the algorithm invokes an Isolate operation on v to mark that v forms a size-1 component in g [u] too.…”

mentioning

confidence: 99%

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Top-K structural diversity search in large networks

Huang

Cheng

et al. 2013

Proc. VLDB Endow.

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Social contagion depicts a process of information (e.g., fads, opinions, news) diffusion in the online social networks. A recent study reports that in a social contagion process the probability of contagion is tightly controlled by the number of connected components in an individual's neighborhood. Such a number is termed structural diversity of an individual and it is shown to be a key predictor in the social contagion process. Based on this, a fundamental issue in a social network is to find top-k users with the highest structural diversities. In this paper, we, for the first time, study the top-k structural diversity search problem in a large network. Specifically, we develop an effective upper bound of structural diversity for pruning the search space. The upper bound can be incrementally refined in the search process. Based on such upper bound, we propose an efficient framework for top-k structural diversity search. To further speed up the structural diversity evaluation in the search process, several carefully devised heuristic search strategies are proposed. Extensive experimental studies are conducted in 13 real-world large networks, and the results demonstrate the efficiency and effectiveness of the proposed methods.

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Section: Related Workmentioning

confidence: 99%

“…After computing score(v * ), the algorithm uses the same process to update the set S by v * as the degree algorithm does (lines [16][17][18][19].…”

Section: Top-k Search Frameworkmentioning

confidence: 99%

Section: : Return Count-components (G[v] T);mentioning

confidence: 99%

“…Then the algorithm iteratively finds the top-k results (lines [6][7][8][9][10][11][12][13][14][15][16][17][18][19] If…”

Section: Top-k Search Frameworkmentioning

confidence: 99%

mentioning

confidence: 99%

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Top-K structural diversity search in large networks

Huang

Cheng

et al. 2013

Proc. VLDB Endow.

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Top-K structural diversity search in large networks

Huang

Cheng

et al. 2015

The VLDB Journal

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Triangle minimization in large networks

2014

Knowl Inf Syst

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The number of triangles is a fundamental metric for analyzing the structure and function of a network. In this paper, for the first time, we investigate the triangle minimization problem in a network under edge (node) attack, where the attacker aims to minimize the number of triangles in the network by removing k edges (nodes). We show that the triangle minimization problem under edge (node) attack is a submodular function maximization problem, which can be solved efficiently. Specifically, we propose a degree-based edge (node) removal algorithm and a near-optimal greedy edge (node) removal algorithm for approximately solving the triangle minimization problem under edge (node) attack. In addition, we introduce two pruning strategies and an approximate marginal gain evaluation technique to further speed up the greedy edge (node) removal algorithm. We conduct extensive experiments over 12 real-world datasets to evaluate the proposed algorithms, and the results demonstrate the effectiveness, efficiency and scalability of our algorithms.

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Scalable Diversified Ranking on Large Graphs

Cited by 19 publications

References 10 publications

Top-K structural diversity search in large networks

Top-K structural diversity search in large networks

Top-K structural diversity search in large networks

Triangle minimization in large networks

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