Centrality Measure Analysis on Protein Interaction Networks

Ali, Anooja; Hulipalled, Vishwanath R; Patil, S. S.

doi:10.1109/temsmet51618.2020.9557447

Cited by 14 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The average global efficiency of the subgraph caused by the node's neighbors is the local efficiency 𝐿 𝐸 of a node in a graph. Therefore, the average local efficiency is the average of local efficiencies of each node is represented as 𝐿 𝐸 (𝐺) = 1 𝑛 ∑ 𝐺 𝐸 (𝐺 𝑠 ) 𝑠∈𝐺 [16] Malaysian Journal of Computer Science, Special Issue on Computing, Communication and Cyber Physical Systems (2022)…”

Section: Resultsmentioning

confidence: 99%

“…These issues need to be addressed to create a network of social network users. Such connected networks are used in various platforms such as network creation [10], community detection [11], [12] structural clustering [13], [14] influential node detection [15], [16] link prediction [17], useful for the development of various technological solutions such as recommendation system [18], [19], recruitment system [20], [21] crime detection [22], and demography [23], etc. Therefore, establishing a connection between LinkedIn profiles of any online social network is a paramount task.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Connecting User Profiles of Social Networks Using Proximity-Based Clustering

Kodabagi

2022

MJCS

View full text Add to dashboard Cite

The establishment of connections among social network users using their profile information is an important task in social network analysis, which facilitates the development of various technological solutions such as stock market analysis, crime detection, tracking system of fraudulent events, etc. In this work, a proximity-based clustering method for networking LinkedIn profiles is presented. The proposed system computes proximity value between users using various attributes of user profiles. The proximity measures are computed by analyzing unstructured data of user profiles to connect users. The method addresses various issues such as comparison of familiar sentences, finding patterns, and sub-patterns among user profiles, assigning weights on attributes similarity, and computing total similarity which is associated with unstructured data. After computing proximity measures on various attributes of user profiles, the connecting edges between nodes are determined by employing artificial intelligence and a network graph is formed. The method is evaluated on a LinkedIn data-set to form a connected graph. The strength of the proposed methodology lies in the formation of multi-layered network graphs, as it uses various attributes of the user profiles to connect them. The proposed methodology helps various applications like recommendation systems to form network graphs of selected attributes and perform the social network analysis. The method achieves an accuracy of 96%. However, the profiles containing abbreviations of important information are not matched and the system accuracy drops down in such cases.

show abstract

Section: Resultsmentioning

confidence: 99%