The community-search problem and how to plan a successful cocktail party

Sozio, Mauro; Gionis, Aristides

doi:10.1145/1835804.1835923

Cited by 406 publications

(406 citation statements)

References 29 publications

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“…It lies in the heart of numerous domains, ranging from text mining [12,22], bioinformatics [25,1], fraud detection [13,2], to community detection [14,20]. Finding dense subgraphs has been studied for many years.…”

Section: Related Workmentioning

confidence: 99%

HiDDen: Hierarchical Dense Subgraph Detection with Application to Financial Fraud Detection

Zhang¹,

Zhou²,

Yildirim³

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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Dense subgraphs are fundamental patterns in graphs, and dense subgraph detection is often the key step of numerous graph mining applications. Most of the existing methods aim to find a single subgraph with a high density. However, dense subgraphs at different granularities could reveal more intriguing patterns in the underlying graph. In this paper, we propose to hierarchically detect dense subgraphs. The key idea of our method (HiDDen) is to envision the density of subgraphs as a relative measure to its background (i.e., the subgraph at the coarse granularity). Given that the hierarchical dense subgraph detection problem is essentially a nonconvex quadratic programming problem, we propose effective and efficient alternative projected gradient based algorithms to solve it. The experimental evaluations on real graphs demonstrate that (1) our proposed algorithms find subgraphs with an up to 40% higher density in almost every hierarchy; (2) the densities of different hierarchies exhibit a desirable variety across different granularities; (3) our projected gradient descent based algorithm scales linearly w.r.t the number of edges of the input graph; and (4) our methods are able to reveal interesting patterns in the underlying graphs (e.g., synthetic ID in financial fraud detection).

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Section: Related Workmentioning

confidence: 99%

HiDDen: Hierarchical Dense Subgraph Detection with Application to Financial Fraud Detection

Zhang¹,

Zhou²,

Yildirim³

et al. 2017

Proceedings of the 2017 SIAM International Conference on Data Mining

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“…[50] show that the above formulation of a densest-subgraph problem with constraints is NP-hard. We thus resort to approximating our optimization objective by the following greedy algorithm.…”

Section: Graph Algorithmmentioning

confidence: 99%

Query-driven on-the-fly knowledge base construction

Nguyen¹,

Abujabal²,

Tran

et al. 2017

Proc. VLDB Endow.

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Today's openly available knowledge bases, such as DBpedia, Yago, Wikidata or Freebase, capture billions of facts about the world's entities. However, even the largest among these (i) are still limited in up-to-date coverage of what happens in the real world, and (ii) miss out on many relevant predicates that precisely capture the wide variety of relationships among entities. To overcome both of these limitations, we propose a novel approach to build on-the-fly knowledge bases in a query-driven manner. Our system, called QKBfly, supports analysts and journalists as well as question answering on emerging topics, by dynamically acquiring relevant facts as timely and comprehensively as possible. QKBfly is based on a semantic-graph representation of sentences, by which we perform three key IE tasks, namely named-entity disambiguation, co-reference resolution and relation extraction, in a light-weight and integrated manner. In contrast to Open IE, our output is canonicalized. In contrast to traditional IE, we capture more predicates, including ternary and higher-arity ones. Our experiments demonstrate that QKBfly can build high-quality, on-the-fly knowledge bases that can readily be deployed, e.g., for the task of ad-hoc question answering. PVLDB Reference Format:

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“…Tsourakakis et al [23] resort to the notion of quasi-clique to define an alternative measure of density, while Wang et al [25] focus on a density based on triangle counting. Sozio et al [21] focus on minimum degree density while enforcing so-called monotone constraints.…”

Section: Related Workmentioning

confidence: 99%

“…spam link farms. In the context of social networks, finding dense subgraphs has been employed for organizing social events and community detection [21], as well as for expert team formation [23,8]. Angel et al [2] have shown how finding dense subgraphs in the entity co-occurrence graph constructed from micro-blogging streams can be used to automatically detect important events.…”

Section: Introductionmentioning

confidence: 99%

Finding Subgraphs with Maximum Total Density and Limited Overlap

Balalau

Bonchi

Chan

et al. 2015

Proceedings of the Eighth ACM International Conference on Web Search and Data Mining

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Finding dense subgraphs in large graphs is a key primitive in a variety of real-world application domains, encompassing social network analytics, event detection, biology, and finance. In most such applications, one typically aims at finding several (possibly overlapping) dense subgraphs which might correspond to communities in social networks or interesting events. While a large amount of work is devoted to finding a single densest subgraph, perhaps surprisingly, the problem of finding several dense subgraphs with limited overlap has not been studied in a principled way, to the best of our knowledge. In this work we define and study a natural generalization of the densest subgraph problem, where the main goal is to find at most k subgraphs with maximum total aggregate density, while satisfying an upper bound on the pairwise Jaccard coefficient between the sets of nodes of the subgraphs. After showing that such a problem is NP-Hard, we devise an efficient algorithm that comes with provable guarantees in some cases of interest, as well as, an efficient practical heuristic. Our extensive evaluation on large realworld graphs confirms the efficiency and effectiveness of our algorithms.

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The community-search problem and how to plan a successful cocktail party

Cited by 406 publications

References 29 publications

HiDDen: Hierarchical Dense Subgraph Detection with Application to Financial Fraud Detection

HiDDen: Hierarchical Dense Subgraph Detection with Application to Financial Fraud Detection

Query-driven on-the-fly knowledge base construction

Finding Subgraphs with Maximum Total Density and Limited Overlap

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