Evolving Networks and Social Network Analysis Methods and Techniques

Abstract

Evolving networks by definition are networks that change as a function of time. They are a natural extension of network science since almost all real-world networks evolve over time, either by adding or by removing nodes or links over time: elementary actor-level network measures like network centrality change as a function of time, popularity and influence of individuals grow or fade depending on processes, and events occur in networks during time intervals. Other problems such as network-level statistics computation, link prediction, community detection, and visualization gain additional research importance when applied to dynamic online social networks (OSNs). Due to their temporal dimension, rapid growth of users, velocity of changes in networks, and amount of data that these OSNs generate, effective and efficient methods and techniques for small static networks are now required to scale and deal with the temporal dimension in case of streaming settings. This chapter reviews the state of the art in selected aspects of evolving social networks presenting open research challenges related to OSNs. The challenges suggest that significant further research is required in evolving social networks, i.e., existent methods, techniques, and algorithms must be rethought and designed toward incremental and dynamic versions that allow the efficient analysis of evolving networks.

The Evolution of the Spatial Association Effect of Carbon Emissions in Transportation: A Social Network Perspective

“…Otherwise, the value was 0. Using this rule, we generated an image matrix [47] as shown in Table 9. When simplifying complex networks, the values in the initial matrix were rearranged to form a series of equivalent image matrices by a cluster analysis method.…”

Analysis of Political Debates through Newspaper Reports: Methods and Outcomes

“…The notion of centrality has been devised to detect influential nodes (the "key players") in a social network, and it reflects the position of a node (in our case an actor, or a claim) within the network. Centrality can be quantified according to different metrics: degree, betweenness, closeness, eigenvector centrality (for a comprehensive review, refer to [5,21,25]). Degree of a node v is calculated as the number of edges incident to v or as the number of nodes directly connected to v. The computation of betweenness and closeness is based on the impact of v on the connectivity of the network: how many shortest paths among other nodes pass through v (betweenness)?…”

“…For both tasks, language specific pre-processing on BERT is important to achieve good results: The German BERT + taz model obtains the best Recall (0.77) and F-Score (0.52) for claim detection and the best F-Score for five of eight major groups and best macro-averaged 5 https://deepset.ai/german-bert. F-Score (0.60) for claim classification.…”

Analysis of Indonesian Motorcycle Gang with Social Network Approach

“…Through graph theory, social network analysis can find out the structure of social relations in certain groups through degree centrality and community detection. The main task of social networking analysis is to identify the most influential actors in a social network using statistical measures [10].…”