Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms

2010 ◽  
Author(s):  
Stephan Gunnemann ◽  
Ines Farber ◽  
Brigitte Boden ◽  
Thomas Seidl
Keyword(s):  
Subspace Clustering ◽  
Dense Subgraph ◽  
Subgraph Mining ◽  
Dense Subgraph Mining

GAMer: a synthesis of subspace clustering and dense subgraph mining

2013 ◽  
Vol 40 (2) ◽  
pp. 243-278 ◽  
Author(s):  
Stephan Günnemann ◽  
Ines Färber ◽  
Brigitte Boden ◽  
Thomas Seidl
Keyword(s):  
Subspace Clustering ◽  
Dense Subgraph ◽  
Subgraph Mining ◽  
Dense Subgraph Mining

Scalable mining of maximal quasi-cliques

2020 ◽  
Vol 14 (4) ◽  
pp. 573-585
Author(s):  
Guimu Guo ◽  
Da Yan ◽  
M. Tamer Özsu ◽  
Zhe Jiang ◽  
Jalal Khalil
Keyword(s):  
Graph Mining ◽  
State Of The Art ◽  
Minimum Degree ◽  
The Other ◽  
Dense Subgraph ◽  
Load Imbalance ◽  
Subgraph Mining ◽  
Execution Engine ◽  
Clique Algorithm ◽  
Dense Subgraph Mining

Given a user-specified minimum degree threshold γ , a γ -quasiclique is a subgraph g = (V g , E g ) where each vertex ν ∈ V g connects to at least γ fraction of the other vertices (i.e., ⌈ γ · (| V g |- 1)⌉ vertices) in g. Quasi-clique is one of the most natural definitions for dense structures useful in finding communities in social networks and discovering significant biomolecule structures and pathways. However, mining maximal quasi-cliques is notoriously expensive. In this paper, we design parallel algorithms for mining maximal quasi-cliques on G-thinker, a distributed graph mining framework that decomposes mining into compute-intensive tasks to fully utilize CPU cores. We found that directly using G-thinker results in the straggler problem due to (i) the drastic load imbalance among different tasks and (ii) the difficulty of predicting the task running time. We address these challenges by redesigning G-thinker's execution engine to prioritize long-running tasks for execution, and by utilizing a novel timeout strategy to effectively decompose long-running tasks to improve load balancing. While this system redesign applies to many other expensive dense subgraph mining problems, this paper verifies the idea by adapting the state-of-the-art quasi-clique algorithm, Quick, to our redesigned G-thinker. Extensive experiments verify that our new solution scales well with the number of CPU cores, achieving 201× runtime speedup when mining a graph with 3.77M vertices and 16.5M edges in a 16-node cluster.

Dense subgraph mining with a mixed graph model

2013 ◽  
Vol 34 (11) ◽  
pp. 1252-1262 ◽  
Author(s):  
Anita Keszler ◽  
Tamás Szirányi ◽  
Zsolt Tuza
Keyword(s):  
Graph Model ◽  
Dense Subgraph ◽  
Mixed Graph ◽  
Subgraph Mining ◽  
Dense Subgraph Mining

scholarly journals Detection of Complexes in Biological Networks Through Diversified Dense Subgraph Mining

2017 ◽  
Vol 24 (9) ◽  
pp. 923-941 ◽  
Author(s):  
Xiuli Ma ◽  
Guangyu Zhou ◽  
Jingbo Shang ◽  
Jingjing Wang ◽  
Jian Peng ◽  
...  
Keyword(s):  
Biological Networks ◽  
Dense Subgraph ◽  
Subgraph Mining ◽  
Dense Subgraph Mining

Subspace Clustering Based on Differential Evolution

2012 ◽  
Vol 35 (10) ◽  
pp. 2116 ◽  
Author(s):  
Zhi-Sheng BI ◽  
Jia-Hai WANG ◽  
Jian YIN
Keyword(s):  
Differential Evolution ◽  
Subspace Clustering

Robust Subspace Clustering Based on Automatic Weighted Multiple Kernel Learning

2021 ◽  
Author(s):  
Guo ◽  
Xiaoqian Zhang ◽  
Zhigui Liu ◽  
Xuqian Xue ◽  
Qian Wang ◽  
...  
Keyword(s):  
Multiple Kernel Learning ◽  
Subspace Clustering ◽  
Kernel Learning ◽  
Multiple Kernel

Low-Rank And Sparse Tensor Representation For Multi-View Subspace Clustering

2021 ◽  
Author(s):  
Shuqin Wang ◽  
Yongyong Chen ◽  
Yigang Ce ◽  
Linna Zhang ◽  
Viacheslav Voronin
Keyword(s):  
Subspace Clustering ◽  
Low Rank ◽  
Tensor Representation
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