Subgraph Augmentation with Application to Graph Mining

Dynamic Graph Mining for Multi-weight Multi-destination Route Planning with Deadlines Constraints

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3412363 ◽

2021 ◽

Vol 15 (1) ◽

pp. 1-32

Author(s):

Yu Huang ◽

Josh Jia-Ching Ying ◽

Philip S. Yu ◽

Vincent S. Tseng

Keyword(s):

Graph Mining ◽

Route Planning ◽

Dynamic Graph

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Discovering configuration templates of virtualized tenant networks in multi-tenancy datacenters via graph-mining

ACM SIGCOMM Computer Communication Review ◽

10.1145/2317307.2317310 ◽

2012 ◽

Vol 42 (3) ◽

pp. 13-20 ◽

Cited By ~ 3

Author(s):

Yosuke Himura ◽

Yoshiko Yasuda

Keyword(s):

Graph Mining

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GiRaF: robust, computational identification of influenza reassortments via graph mining

Nucleic Acids Research ◽

10.1093/nar/gkq1232 ◽

2010 ◽

Vol 39 (6) ◽

pp. e34-e34 ◽

Cited By ~ 37

Author(s):

Niranjan Nagarajan ◽

Carl Kingsford

Keyword(s):

Graph Mining ◽

Computational Identification

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Graph mining assisted semi-supervised learning for fraudulent cash-out detection

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 - ASONAM '17 ◽

10.1145/3110025.3110099 ◽

2017 ◽

Cited By ~ 1

Author(s):

Yuan Li ◽

Yiheng Sun ◽

Noshir Contractor

Keyword(s):

Supervised Learning ◽

Graph Mining

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Scalable mining of maximal quasi-cliques

Proceedings of the VLDB Endowment ◽

10.14778/3436905.3436916 ◽

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.

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