Evolution of Ego-networks in Social Media with Link Recommendations

Ego-networks are fundamental structures in social graphs, yet the process of their evolution is still widely unexplored. In an online context, a key question is how link recommender systems may skew the growth of these networks, possibly restraining diversity. To shed light on this matter, we analyze the complete temporal evolution of 170M ego-networks extracted from Flickr and Tumblr, comparing links that are created spontaneously with those that have been algorithmically recommended. We find that the evolution of ego-networks is bursty, community-driven, and characterized by subsequent phases of explosive diameter increase, slight shrinking, and stabilization. Recommendations favor popular and well-connected nodes, limiting the diameter expansion. With a matching experiment aimed at detecting causal relationships from observational data, we find that the bias introduced by the recommendations fosters global diversity in the process of neighbor selection. Last, with two link prediction experiments, we show how insights from our analysis can be used to improve the effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl

Principal Patterns on Graphs: Discovering Coherent Structures in Datasets

Graphs are now ubiquitous in almost every field of research. Recently, new research areas devoted to the analysis of graphs and data associated to their vertices have emerged. Focusing on dynamical processes, we propose a fast, robust and scalable framework for retrieving and analyzing recurring patterns of activity on graphs. Our method relies on a novel type of multilayer graph that encodes the spreading or propagation of events between successive time steps. We demonstrate the versatility of our method by applying it on three different real-world examples. Firstly, we study how rumor spreads on a social network. Secondly, we reveal congestion patterns of pedestrians in a train station. Finally, we show how patterns of audio playlists can be used in a recommender system. In each example, relevant information previously hidden in the data is extracted in a very efficient manner, emphasizing the scalability of our method. With a parallel implementation scaling linearly with the size of the dataset, our framework easily handles millions of nodes on a single commodity server

Concept drift from 1980 to 2020: a comprehensive bibliometric analysis with future research insight

In nonstationary environments, high-dimensional data streams have been generated unceasingly where the underlying distribution of the training and target data may change over time. These drifts are labeled as concept drift in the literature. Learning from evolving data streams demands adaptive or evolving approaches to handle concept drifts, which is a brand-new research affair. In this effort, a wide-ranging comparative analysis of concept drift is represented to highlight state-of-the-art approaches, embracing the last four decades, namely from 1980 to 2020. Considering the scope and discipline; the core collection of the Web of Science database is regarded as the basis of this study, and 1,564 publications related to concept drift are retrieved. As a result of the classification and feature analysis of valid literature data, the bibliometric indicators are revealed at the levels of countries/regions, institutions, and authors. The overall analyses, respecting the publications, citations, and cooperation of networks, are unveiled not only the highly authoritative publications but also the most prolific institutions, influential authors, dynamic networks, etc. Furthermore, deep analyses including text mining such as; the burst detection analysis, co-occurrence analysis, timeline view analysis, and bibliographic coupling analysis are conducted to disclose the current challenges and future research directions. This paper contributes as a remarkable reference for invaluable further research of concept drift, which enlightens the emerging/trend topics, and the possible research directions with several graphs, visualized by using the VOS viewer and Cite Space software