Mining coherent dense subgraphs across massive biological networks for functional discovery

Hu, Haiyan; Yan, Xifeng; Huang, Yu; Han, Jiawei; Zhou, Xianghong Jasmine

doi:10.1093/bioinformatics/bti1049

Cited by 282 publications

(249 citation statements)

References 20 publications

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“…In a similar direction as the previous paper, Hu et al propose a framework for mining frequent coherent dense subgraphs across a sequence of biological networks [31]. Their core concept is to construct a second-order graph, which represents co-activity of edges in the initial graph.…”

Section: Interaction-based Community Detectionmentioning

confidence: 93%

Discovering Dynamic Communities in Interaction Networks

Rozenshtein

Tatti

Gionis

2014

Machine Learning and Knowledge Discovery in Databases

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Very often online social networks are defined by aggregating information regarding the interaction between the nodes of the network. For example, a call graph is defined by considering an edge for each pair of individuals who have called each other at least once -or at least k times. Similarly, an implicit social network in a social-media site is defined by considering an edge for each pair of users who have interacted in some way, e.g., have made a conversation, commented to each other's content, etc. Despite the fact that this type of definitions have been used to obtain a lot of insights regarding the structure of social networks, it is obvious that they suffer from a severe limitation: they neglect the precise time that the interaction between network nodes occurs.In this thesis we propose to study interaction networks, where one considers not only the underlying topology of the social network, but also the exact time instances that nodes interact. In an interaction network an edge is associated with a time stamp, and multiple edges may occur for the same pair of nodes. Consequently, interaction networks offer a more fine-grained representation that can be used to reveal otherwise hidden dynamic phenomena in the network.In the context of interaction networks, we study the problem of discovering communities, which are dense in terms of the underlying network structure, and whose edges occur in short time intervals. Such communities represent groups of individuals who interact with each other in some specific time instances, for example, a group of employees who work on a project and whose interaction intensifies before certain project milestones. We prove that the problem we define is NP-hard, and we provide effective algorithms by adapting techniques used to find dense subgraphs. We perform extensive evaluation of the proposed methods on synthetic and real datasets, which demonstrates the validity of our concepts and the good performance of our algorithms.

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Section: Interaction-based Community Detectionmentioning

confidence: 93%

Discovering Dynamic Communities in Interaction Networks

Rozenshtein

Tatti

Gionis

2014

Machine Learning and Knowledge Discovery in Databases

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“…These applications include Bioinformatics [1], [2], Pattern Recognition [3], XML documents [4], Chemical compounds [5], Social networks [6], etc. All these applications indicate the importance and the broad usage of graph databases.…”

Section: Introductionmentioning

confidence: 99%

A similarity skyline approach for handling graph queries - A preliminary report

Abbaci

Hadjali

Liétard

et al. 2011

2011 IEEE 27th International Conference on Data Engineering Workshops

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Abstract-One of the fundamental problems in graph databases is similarity search for graphs of interest. Existing approaches dealing with this problem rely on a single similarity measure between graph structures. In this paper, we suggest an alternative approach allowing for searching similar graphs to a graph query where similarity between graphs is rather modeled by a vector of scalars than a unique scalar. To this end, we introduce the notion of similarity skyline of a graph query defined by the subset of graphs of the target database that are the most similar to the query in a Pareto sense. The idea is to achieve a d-dimensional comparison between graphs in terms of local distance (or similarity) measures and to retrieve those graphs that are maximally similar in the sense of the Pareto dominance relation. A diversity-based method for refining the retrieval result is proposed as well.

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“…The effectiveness of such approaches can be enhanced if templates accommodate frequent query patterns. Algorithms for discovering such patterns have been developed in areas with a large amount of data, such as biology and medicine ( [9,4]) and social networks ( [12]), but with no separation between the syntactic and the semantic layers of these patterns. In this paper we make the case that the "right questions" in a domain can be captured through query patterns that are characteristic of the domain of discourse.…”

Section: Introductionmentioning

confidence: 99%

Developing Query Patterns

Constantopoulos

Dritsou

Foustoucos

2009

Research and Advanced Technology for Digital Libraries

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Abstract. Query patterns enable effective information tools and provide guidance to users interested in posing complex questions about objects. Semantically, query patterns represent important questions, while syntactically they impose the correct formulation of queries. In this paper we address the development of query patterns at successive representation layers so as to expose dominant information requirements on one hand, and structures that can support effective user interaction and efficient implementation of query processing on the other. An empirical study for the domain of cultural heritage reveals an initial set of recurrent questions, which are then reduced to a modestly sized set of query patterns. A set of Datalog rules is developed in order to formally define these patterns which are also expressed as SPARQL queries.

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Mining coherent dense subgraphs across massive biological networks for functional discovery

Abstract: http://zhoulab.usc.edu/CODENSE/

Cited by 282 publications

References 20 publications

Discovering Dynamic Communities in Interaction Networks

Discovering Dynamic Communities in Interaction Networks

A similarity skyline approach for handling graph queries - A preliminary report

Developing Query Patterns

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