network flow model social network analysis definition

The information obtained by analyzing social networks could be especially valuable for many applications.

The analysis of social networks helps summarizing the interests and opinions of users (nodes), discovering patterns from the interactions (links) between users, and mining the events that take place in online platforms. Social networks, such as Facebook, Twitter, and LinkedIn, have greatly facilitated communication between web users around the world. Finally, we discuss some promising research directions for future work.

We then introduce techniques for embedding subgraphs and also present the applications of network representation learning. We will also introduce some extensions of the base models, tackling more complex scenarios such as analyzing attributed networks, heterogeneous networks, and dynamic networks.

First, we introduce the basic models for learning node representations in homogeneous networks. In this survey, we conduct a comprehensive review of the current literature in network representation learning, utilizing neural network models. In addition, techniques based on deep neural networks have attracted great interests over the past a few years. Network representation leaning facilitates further applications such as classification, link prediction, anomaly detection, and clustering. A fundamental step for analyzing social networks is to encode network data into low-dimensional representations, i.e., network embeddings, so that the network topology structure and other attribute information can be effectively preserved. Social network analysis is an important problem in data mining. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, United States.

Network flow model social network analysis definition