NLP, Data Mining, IR, ML
Title of Thesis
A New Clustering Algorithm for Attributive Graphs through Information Diffusion Approaches
Description of Thesis
Simplicity and flexibility constitute the two basic features for graph models, which has made them functional models for real-life problems. The attributive graphs are too popular among researchers because of their efficiency and functionality. An attributive graph is a graph the nodes and edges of which can be attributive. Nodes and edges as structural dimension and their attributes as contextual dimension made graphs more flexible in modeling real problems. In this study, a new clustering algorithm is proposed based on K-Medoid which focuses on the graph’s structure dimension, through heat diffusion algorithm and contextual dimension through weighted Jaccard coefficient in a simultaneous matter. The calculated clusters through the proposed algorithm are denser and nodes with more similar attributes. DBLP and PBLOG real data sets are applied to evaluate and compare this algorithm with new and well-known cluster algorithms. Results indicate the outperformers of this algorithm in relation to its counterparts as to structure quality, cluster contextual and time complexity criteria.