دکتر محمدعلی جوادزاده

Graphs are data that describe complex relationships between different things in the real world, such as the Internet, social network, bibliographic network, and so on. One of the things that many people deal with today is online social networks. The graph display of online social networks such as Twitter, WeChat and Facebook is not possible today with less than billions of nodes, and for this reason, the study of large-scale network data has become a necessity for researchers. Regarding social networks, online users often have limited information; But for social media service providers, user node information such as interest, beliefs, or other characteristics are very important to customize their services for users in many applications such as recommendations and personalized search, making it a challenge for service providers. An effective way to deal with this challenge is to infer missing user information using pervasive network structures in social media. One of the most important inferences in data mining and network analysis is node classification, which aims to infer the missing labels of nodes based on labeled nodes and network structure. In this research, we have performed the task of node classification on the PubMedDiabetes, CiteSeer and Cora citation network datasets using GraphSAGE, GCN and GAT neural networks and we have generally concluded that the GraphSAGE neural network on the network datasets The cited reference works well for the node classification task.

Researchers have always been interested in graph nodes clustering based on content or structure. But less attention has been paid to clustering based on both structure and content. But a content-structural clustering is needed in information networks like social networks. In this paper, the ICS-Cluster algorithm is proposed which takes into consideration both the structure and content aspects of the nodes. The purpose of this approach is to gain a coherent internal structure (structural aspect) and homogeneous attribute values (content aspect) in the graph. In this approach firstly the graph is converted into a content-structural graph which edges’ weight show similarity between the connected nodes. Incremental clustering is done based on edges’ weight in this process the edges with the most weight is considered as clusters then the weight of connected edge to the cluster is aggregated and they’ll be one edge, the process is repeated until the algorithm reaches the number of clusters that indicated by the user. ICS-Cluster algorithm number of cluster is indicated by the user. Comparing ICS-Cluster with other content structural algorithm based on six criteria for measuring cluster quality shows that ICS-Cluster has good performance. These criteria contain structural criteria (Modularity, Error Link, and Density), content criterion (Average Similarity), content-structural criterion (CS-Measure) and the run time.