On relational learning and discovery in social networks: a survey

Abstract

The social networking scene has evolved tremendously over the years. It has grown in relational complexities that extend a vast presence into popular social media platforms on the internet. With the advance of sentimental computing and social complexity, relationships which were once thought to be simple have now become multi-dimensional and widespread in the online scene. This explosion of data in the online social scene has attracted much research attention. The main aims of this work revolve around the knowledge discovery and datamining processes of these feature-rich relations. In this paper, we provide a survey of relational learning and discovery through popular social analysis of different structure types which are integral to applications within the emerging field of sentimental and affective computing. It is hoped that this contribution will add to the clarity of how social networks are analyzed with the latest groundbreaking methods and provide certain directions for future improvements.

A Survey on Contact Tracing: the Latest Advancements and Challenges

“…Traditional methods are fundamentally linked to the individual-level spread of infection and, in particular, the network of potential transmission routes. As a general and popular data structure to represent complex relationships between entities such as in society [21] [22] and biology [23], network has been also an important concept in human disease modeling, where social interactions can take place over a wide range of distances. While some models have been developed to consider the role of contact tracing in randomly interacting populations [8], only network-based models that consider transmission pathways [24] and the associated pairwise equations can provide an accurate mechanistic understanding of the structured nature of human interactions.…”

A Survey on Dynamic Network Embedding

“…Network analysis has attracted increasing attention over the recent years due to the ubiquity of network data in real world. The graph-structured network is a information carrier commonly used in complex systems, such as semantic networks [46], proteinprotein interaction networks [55], social networks [61] and criminal networks [51]. In order to construct the feature representations that can be applied to various tasks on graph-structured networks, network embedding is proposed to embed each node in the network into low-dimensional space [7].…”