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A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
Social network analysis: An overview
S Tabassum, FSF Pereira… - … Reviews: Data Mining …, 2018 - Wiley Online Library
Social network analysis (SNA) is a core pursuit of analyzing social networks today. In
addition to the usual statistical techniques of data analysis, these networks are investigated …
addition to the usual statistical techniques of data analysis, these networks are investigated …
Influence maximization in social networks using graph embedding and graph neural network
With the boom in technologies and mobile networks in recent years, online social networks
have become an integral part of our daily lives. These virtual networks connect people …
have become an integral part of our daily lives. These virtual networks connect people …
Finding key players in complex networks through deep reinforcement learning
Finding an optimal set of nodes, called key players, whose activation (or removal) would
maximally enhance (or degrade) a certain network functionality, is a fundamental class of …
maximally enhance (or degrade) a certain network functionality, is a fundamental class of …
[ספר][B] Recommender systems
CC Aggarwal - 2016 - Springer
“Nature shows us only the tail of the lion. But I do not doubt that the lion belongs to it even
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …
though he cannot at once reveal himself because of his enormous size.”–Albert Einstein The …
Vital nodes identification in complex networks
Real networks exhibit heterogeneous nature with nodes playing far different roles in
structure and function. To identify vital nodes is thus very significant, allowing us to control …
structure and function. To identify vital nodes is thus very significant, allowing us to control …
A survey of link prediction in complex networks
V Martínez, F Berzal, JC Cubero - ACM computing surveys (CSUR), 2016 - dl.acm.org
Networks have become increasingly important to model complex systems composed of
interacting elements. Network data mining has a large number of applications in many …
interacting elements. Network data mining has a large number of applications in many …
Influence maximization in complex networks through optimal percolation
The whole frame of interconnections in complex networks hinges on a specific set of
structural nodes, much smaller than the total size, which, if activated, would cause the …
structural nodes, much smaller than the total size, which, if activated, would cause the …
Influence maximization in near-linear time: A martingale approach
Given a social network G and a positive integer k, the influence maximization problem asks
for k nodes (in G) whose adoptions of a certain idea or product can trigger the largest …
for k nodes (in G) whose adoptions of a certain idea or product can trigger the largest …
Measuring user influence on Twitter: A survey
F Riquelme, P González-Cantergiani - Information processing & …, 2016 - Elsevier
Centrality is one of the most studied concepts in social network analysis. There is a huge
literature regarding centrality measures, as ways to identify the most relevant users in a …
literature regarding centrality measures, as ways to identify the most relevant users in a …