Abstract
Depicting a complex system like social networks as a graph helps understand its structure and relation. As advances in technology increase the amount of data, simplifying a large-scale graph has attracted interests. Simplification reduces the size of a graph while preserving its important properties. In this paper, we propose the summarization algorithm to simplify a graph focusing on degree correlation and clustering coefficient. The degree correlation is a measure to assess the influence of each vertex and their connections. The clustering coefficient estimates latent connections between two distinct vertices. To this end, we first separate a graph into communities. Looking at groups instead of a graph itself allows us to extract important vertices and edges more easily. We then categorize communities into four cases and simplify them in different ways to preserve the innate characteristics. The quantitative and qualitative evaluations demonstrate how effectively our algorithm serves the goal. Overall, our contributions are as follows: (a) unique pattern: We found that hubs are connected indirectly via low-degree vertices. Sigcon preserves these vertices during simplification. (b) efficient algorithm: Sigcon identifies influential vertices and edges to simplify a graph on the basis of degree correlation and clustering coefficient connecting vertices effectively.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 372-379 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781538625880 |
| DOIs | |
| Publication status | Published - 2018 Feb 14 |
| Event | 19th IEEE Intl Conference on High Performance Computing and Communications, 15th IEEE Intl Conference on Smart City, and 3rd IEEE Intl Conference on Data Science and Systems, HPCC/SmartCity/DSS 2017 - Bangkok, Thailand Duration: 2017 Dec 18 → 2017 Dec 20 |
Publication series
| Name | Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017 |
|---|---|
| Volume | 2018-January |
Other
| Other | 19th IEEE Intl Conference on High Performance Computing and Communications, 15th IEEE Intl Conference on Smart City, and 3rd IEEE Intl Conference on Data Science and Systems, HPCC/SmartCity/DSS 2017 |
|---|---|
| Country | Thailand |
| City | Bangkok |
| Period | 17/12/18 → 17/12/20 |
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All Science Journal Classification (ASJC) codes
- Information Systems
- Hardware and Architecture
- Computer Science Applications
- Computer Networks and Communications
- Computational Theory and Mathematics
- Software
Cite this
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Sigcon : Simplifying a Graph Based on Degree Correlation and Clustering Coefficient. / Jung, Hojin; Kim, Songkuk.
Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 372-379 (Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017; Vol. 2018-January).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Sigcon
T2 - Simplifying a Graph Based on Degree Correlation and Clustering Coefficient
AU - Jung, Hojin
AU - Kim, Songkuk
PY - 2018/2/14
Y1 - 2018/2/14
N2 - Depicting a complex system like social networks as a graph helps understand its structure and relation. As advances in technology increase the amount of data, simplifying a large-scale graph has attracted interests. Simplification reduces the size of a graph while preserving its important properties. In this paper, we propose the summarization algorithm to simplify a graph focusing on degree correlation and clustering coefficient. The degree correlation is a measure to assess the influence of each vertex and their connections. The clustering coefficient estimates latent connections between two distinct vertices. To this end, we first separate a graph into communities. Looking at groups instead of a graph itself allows us to extract important vertices and edges more easily. We then categorize communities into four cases and simplify them in different ways to preserve the innate characteristics. The quantitative and qualitative evaluations demonstrate how effectively our algorithm serves the goal. Overall, our contributions are as follows: (a) unique pattern: We found that hubs are connected indirectly via low-degree vertices. Sigcon preserves these vertices during simplification. (b) efficient algorithm: Sigcon identifies influential vertices and edges to simplify a graph on the basis of degree correlation and clustering coefficient connecting vertices effectively.
AB - Depicting a complex system like social networks as a graph helps understand its structure and relation. As advances in technology increase the amount of data, simplifying a large-scale graph has attracted interests. Simplification reduces the size of a graph while preserving its important properties. In this paper, we propose the summarization algorithm to simplify a graph focusing on degree correlation and clustering coefficient. The degree correlation is a measure to assess the influence of each vertex and their connections. The clustering coefficient estimates latent connections between two distinct vertices. To this end, we first separate a graph into communities. Looking at groups instead of a graph itself allows us to extract important vertices and edges more easily. We then categorize communities into four cases and simplify them in different ways to preserve the innate characteristics. The quantitative and qualitative evaluations demonstrate how effectively our algorithm serves the goal. Overall, our contributions are as follows: (a) unique pattern: We found that hubs are connected indirectly via low-degree vertices. Sigcon preserves these vertices during simplification. (b) efficient algorithm: Sigcon identifies influential vertices and edges to simplify a graph on the basis of degree correlation and clustering coefficient connecting vertices effectively.
UR - http://www.scopus.com/inward/record.url?scp=85047460927&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047460927&partnerID=8YFLogxK
U2 - 10.1109/HPCC-SmartCity-DSS.2017.49
DO - 10.1109/HPCC-SmartCity-DSS.2017.49
M3 - Conference contribution
AN - SCOPUS:85047460927
T3 - Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017
SP - 372
EP - 379
BT - Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -