Statistical inference links data and theory in network science
The number of network science applications across many different fields has been rapidly
increasing. Surprisingly, the development of theory and domain-specific applications often …
increasing. Surprisingly, the development of theory and domain-specific applications often …
Link prediction techniques, applications, and performance: A survey
Link prediction finds missing links (in static networks) or predicts the likelihood of future links
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …
(in dynamic networks). The latter definition is useful in network evolution (Wang et al., 2011; …
Representation learning for attributed multiplex heterogeneous network
Network embedding (or graph embedding) has been widely used in many real-world
applications. However, existing methods mainly focus on networks with single-typed …
applications. However, existing methods mainly focus on networks with single-typed …
Gmnn: Graph markov neural networks
This paper studies semi-supervised object classification in relational data, which is a
fundamental problem in relational data modeling. The problem has been extensively studied …
fundamental problem in relational data modeling. The problem has been extensively studied …
A review of relational machine learning for knowledge graphs
Relational machine learning studies methods for the statistical analysis of relational, or
graph-structured, data. In this paper, we provide a review of how such statistical models can …
graph-structured, data. In this paper, we provide a review of how such statistical models can …
Link prediction in complex networks: A survey
L Lü, T Zhou - Physica A: statistical mechanics and its applications, 2011 - Elsevier
Link prediction in complex networks has attracted increasing attention from both physical
and computer science communities. The algorithms can be used to extract missing …
and computer science communities. The algorithms can be used to extract missing …
Mining heterogeneous information networks: a structural analysis approach
Most objects and data in the real world are of multiple types, interconnected, forming
complex, heterogeneous but often semi-structured information networks. However, most …
complex, heterogeneous but often semi-structured information networks. However, most …
Sisa: Set-centric instruction set architecture for graph mining on processing-in-memory systems
Simple graph algorithms such as PageRank have been the target of numerous hardware
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
accelerators. Yet, there also exist much more complex graph mining algorithms for problems …
Graphmix: Improved training of gnns for semi-supervised learning
We present GraphMix, a regularization method for Graph Neural Network based semi-
supervised object classification, whereby we propose to train a fully-connected network …
supervised object classification, whereby we propose to train a fully-connected network …