Analysis of co-authorship in computer networks using centrality measures

Abstract

Assorted communication on social networks attracted researcher's attention in recent time. Massive sharing of opinions, ideas, experiences and expertise highlight communication through social networks. Social networks have becoming a flourishing network for sharing such values. Identifying and analyzing these communication trends have gained importance for detecting patterns among peoples in social networks using community detection. Different methods have been proposed for detecting communities in social networks. Co-authorship network is also becoming central point of attention to many researchers. Domain specific co-authorship community detection is evolving area that is emerging beside other community detection identification patterns. In this paper, we performed analysis on coauthorship network in the field of computer networks using common centrality measures. Finding reveals that this study finds domain specific co-authors that are best in their knowledge sharing and collaboration in the field of computer networks.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 9 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.