Finding dense subgraphs

1995 ◽  
pp. 102-111 ◽  
Author(s):  
Yuichi Asahiro ◽  
Kazuo Iwama
Keyword(s):  
Dense Subgraphs

scholarly journals Space- and Time-Efficient Algorithm for Maintaining Dense Subgraphs on One-Pass Dynamic Streams

2015 ◽  
Author(s):  
Sayan Bhattacharya ◽  
Monika Henzinger ◽  
Danupon Nanongkai ◽  
Charalampos Tsourakakis
Keyword(s):  
Efficient Algorithm ◽  
Dense Subgraphs ◽  
Space And Time

Finding Dense Subgraphs with Size Bounds

2009 ◽  
pp. 25-37 ◽  
Author(s):  
Reid Andersen ◽  
Kumar Chellapilla
Keyword(s):  
Dense Subgraphs

Dense subgraphs of power-law random graphs

2021 ◽  
Vol 10 (1) ◽  
pp. 1-11
Author(s):  
Denis O. Lazarev ◽  
Nikolay N. Kuzyurin
Keyword(s):  
Random Graphs ◽  
Power Law ◽  
Dense Subgraphs

Finding Dense Subgraphs in Relational Graphs

2015 ◽  
pp. 641-654 ◽  
Author(s):  
Vinay Jethava ◽  
Niko Beerenwinkel
Keyword(s):  
Dense Subgraphs ◽  
Relational Graphs

Test dense subgraphs in sparse uniform hypergraph

2020 ◽  
pp. 1-20
Author(s):  
Mingao Yuan ◽  
Yehong Nan
Keyword(s):  
Uniform Hypergraph ◽  
Dense Subgraphs

scholarly journals Finding events in temporal networks: segmentation meets densest subgraph discovery

2019 ◽  
Vol 62 (4) ◽  
pp. 1611-1639
Author(s):  
Polina Rozenshtein ◽  
Francesco Bonchi ◽  
Aristides Gionis ◽  
Mauro Sozio ◽  
Nikolaj Tatti
Keyword(s):  
Optimization Problem ◽  
Approximate Dynamic Programming ◽  
Problem Formulation ◽  
Network Activity ◽  
Total Density ◽  
Temporal Networks ◽  
Time Intervals ◽  
Dense Subgraphs ◽  
Approximation Guarantee ◽  
Densest Subgraph

Abstract In this paper, we study the problem of discovering a timeline of events in a temporal network. We model events as dense subgraphs that occur within intervals of network activity. We formulate the event discovery task as an optimization problem, where we search for a partition of the network timeline into k non-overlapping intervals, such that the intervals span subgraphs with maximum total density. The output is a sequence of dense subgraphs along with corresponding time intervals, capturing the most interesting events during the network lifetime. A naïve solution to our optimization problem has polynomial but prohibitively high running time. We adapt existing recent work on dynamic densest subgraph discovery and approximate dynamic programming to design a fast approximation algorithm. Next, to ensure richer structure, we adjust the problem formulation to encourage coverage of a larger set of nodes. This problem is NP-hard; however, we show that on static graphs a simple greedy algorithm leads to approximate solution due to submodularity. We extend this greedy approach for temporal networks, but we lose the approximation guarantee in the process. Finally, we demonstrate empirically that our algorithms recover solutions with good quality.

Efficient multi-target tracking via discovering dense subgraphs

2016 ◽  
Vol 144 ◽  
pp. 205-216 ◽  
Author(s):  
Behzad Bozorgtabar ◽  
Roland Goecke
Keyword(s):  
Target Tracking ◽  
Dense Subgraphs ◽  
Multi Target Tracking

scholarly journals Protein complex prediction via dense subgraphs and false positive analysis

PLoS ONE ◽  
2017 ◽  
Vol 12 (9) ◽  
pp. e0183460 ◽  
Author(s):  
Cecilia Hernandez ◽  
Carlos Mella ◽  
Gonzalo Navarro ◽  
Alvaro Olivera-Nappa ◽  
Jaime Araya
Keyword(s):  
Protein Complex ◽  
False Positive ◽  
Dense Subgraphs ◽  
Protein Complex Prediction ◽  
Positive Analysis

scholarly journals Guaranteed Recovery of Planted Cliques and Dense Subgraphs by Convex Relaxation

2015 ◽  
Vol 167 (2) ◽  
pp. 653-675 ◽  
Author(s):  
Brendan P. W. Ames
Keyword(s):  
Convex Relaxation ◽  
Dense Subgraphs
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