چكيده به لاتين
In recent years, the data of various networks has grown at an extraordinary rate. These networks exist in many areas such as the Internet, the World Wide Web, scientific citation and authorship, epidemiology, communication analysis, metabolism, ecosystem, bioinformatics, fraud and terrorist analysis, and many more. The links in the network data may represent citations, friendships, associations, metabolic functions, communications, common sites, common mechanisms, or many other explicit or implicit relationships.
In the present age, one of the most important networks in which we play role through making daily interactions with other people is social networks. These networks have entered a new phase with the growth and expansion of the World Wide Web. Due to the diversity of social networks, studying and recognizing the characteristics of these networks requires expertise in various social fields. However, discovering and extracting features manually has its own challenges. one of these challenges is that some important and effective features have not been discovered in the manual feature extraction process which results in the lack of accurate understanding of these networks.
In recent years, graph embedding methods have been proposed as methods to study graph properties. Each node is represented by a feature vector in the network. In fact, each element of this vector indicates a property of the node in the network. Network embedding which is a method for learning low-dimensional representations of nodes is useful in many network study applications such as edge prediction and node clustering.
Most of these social networks are naturally dynamic due to the individuals 'interactions inside of it, that is, they evolve over time as nodes and links are added, removed, and changed. in social networks, temporal information is very important for accurate modeling, forecasting and understanding the network data. Despite the importance of these dynamics, most research on network representation learning has focused on static graphs. with regard to the dynamic nature of social networks and the inefficiency of static methods for studying dynamic social networks, we aimed to provide a general framework for automatically extracting the features of dynamic social networks by combining temporal information in static embedding methods.
