Abstract
Recent advancements in machine learning algorithms have transformed the data analytics domain and provided innovative solutions to inherently difficult problems. However, training models at scale over large data sets remains a daunting challenge. One such problem is the detection of overlapping communities within graphs. For example, a social network can be modeled as a graph where the vertices and edges represent individuals and their relationships. As opposed to the problem of graph partitioning or clustering, an individual can be part of multiple communities which significantly increases the problem complexity. In this paper, we present and evaluate an efficient parallel and distributed implementation of a Stochastic Gradient Markov Chain Monte Carlo algorithm that solves the overlapping community detection problem. We show that the algorithm can scale and process graphs consisting of billions of edges and tens of millions of vertices on a compute cluster of 65 nodes. To the best of our knowledge, this is the first time that the problem of deducing overlapping communities has been learned for problems of such a large scale.
| Language | English |
|---|---|
| Title of host publication | Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016 |
| Publisher | ACM, IEEE Computer Society |
| Pages | 1463-1472 |
| Number of pages | 10 |
| Volume | 2016-August |
| ISBN (Electronic) | 9781509021406 |
| DOIs | |
| Publication status | Published - 2 Aug 2016 |
| Event | 30th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016 - Chicago, United States Duration: 23 May 2016 → 27 May 2016 |
Conference
| Conference | 30th IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016 |
|---|---|
| Country | United States |
| City | Chicago |
| Period | 23/05/16 → 27/05/16 |
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Keywords
- Distributed computing
- High performance computing
- Machine learning
- Parallel programming
- Performance analysis
Cite this
}
Scalable overlapping community detection. / Elhelw, Ismail; Hofman, Rutger; Li, Wenzhe; Ahn, Sungjin; Welling, Max; Bal, Henri.
Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016. Vol. 2016-August ACM, IEEE Computer Society, 2016. p. 1463-1472 7530037.Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Scalable overlapping community detection
AU - Elhelw, Ismail
AU - Hofman, Rutger
AU - Li, Wenzhe
AU - Ahn, Sungjin
AU - Welling, Max
AU - Bal, Henri
PY - 2016/8/2
Y1 - 2016/8/2
N2 - Recent advancements in machine learning algorithms have transformed the data analytics domain and provided innovative solutions to inherently difficult problems. However, training models at scale over large data sets remains a daunting challenge. One such problem is the detection of overlapping communities within graphs. For example, a social network can be modeled as a graph where the vertices and edges represent individuals and their relationships. As opposed to the problem of graph partitioning or clustering, an individual can be part of multiple communities which significantly increases the problem complexity. In this paper, we present and evaluate an efficient parallel and distributed implementation of a Stochastic Gradient Markov Chain Monte Carlo algorithm that solves the overlapping community detection problem. We show that the algorithm can scale and process graphs consisting of billions of edges and tens of millions of vertices on a compute cluster of 65 nodes. To the best of our knowledge, this is the first time that the problem of deducing overlapping communities has been learned for problems of such a large scale.
AB - Recent advancements in machine learning algorithms have transformed the data analytics domain and provided innovative solutions to inherently difficult problems. However, training models at scale over large data sets remains a daunting challenge. One such problem is the detection of overlapping communities within graphs. For example, a social network can be modeled as a graph where the vertices and edges represent individuals and their relationships. As opposed to the problem of graph partitioning or clustering, an individual can be part of multiple communities which significantly increases the problem complexity. In this paper, we present and evaluate an efficient parallel and distributed implementation of a Stochastic Gradient Markov Chain Monte Carlo algorithm that solves the overlapping community detection problem. We show that the algorithm can scale and process graphs consisting of billions of edges and tens of millions of vertices on a compute cluster of 65 nodes. To the best of our knowledge, this is the first time that the problem of deducing overlapping communities has been learned for problems of such a large scale.
KW - Distributed computing
KW - High performance computing
KW - Machine learning
KW - Parallel programming
KW - Performance analysis
UR - http://www.scopus.com/inward/record.url?scp=84991688473&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84991688473&partnerID=8YFLogxK
U2 - 10.1109/IPDPSW.2016.165
DO - 10.1109/IPDPSW.2016.165
M3 - Conference contribution
VL - 2016-August
SP - 1463
EP - 1472
BT - Proceedings - 2016 IEEE 30th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2016
PB - ACM, IEEE Computer Society
ER -