التفاصيل البيبلوغرافية
| العنوان: |
Community detection in Networks using Graph Embedding. |
| المؤلفون: |
Agrawal, Rimjhim, Arquam, Md., Singh, Anurag |
| المصدر: |
Procedia Computer Science; 2020, Vol. 173, p372-381, 10p |
| مصطلحات موضوعية: |
ONLINE social networks, DEMOGRAPHIC surveys, BIOLOGICAL networks, VECTOR spaces, TELECOMMUNICATION systems, SOCIAL network theory |
| مستخلص: |
Graphs are used to depict real-world scenarios as they have a wide variety such as online social networks, data and communication networks, word co-occurrence networks, biological networks, transport networks etc. Rigorous analysis of graphs produces the insight of graphs and yields the more profound knowledge of the social structure, language, and different communication patterns through data analysis of each type and provide the interaction between them. Many applications like node classification, link prediction, clustering can be inferred by using the graph. Several proposals are formulated to perform graph analysis. Recently, the most popular methods used by researchers are the presentation of graph nodes in a vector space by transforming the graph information into a low-dimensional manifold with maximum preservation of graph properties. In this course of the research, researchers apply the embedding technique to classify nodes of a graph based on the intrinsic property of nodes. However, most graph analytic methods require excessive space and computation cost. Therefore, a new method of embedding is needed for graph analysis. In this paper, various existing embedding techniques are simulated and comparatively discussed with different datasets. Based on this embedding, communities are created on the real-world network data of the karate club. At last, some future direction of research is mentioned in terms of application scenarios. [ABSTRACT FROM AUTHOR] |
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