Graph summarization methods and applications: A survey
While advances in computing resources have made processing enormous amounts of data
possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …
possible, human ability to identify patterns in such data has not scaled accordingly. Efficient …
Continuous-time dynamic network embeddings
Networks evolve continuously over time with the addition, deletion, and changing of links
and nodes. Although many networks contain this type of temporal information, the majority of …
and nodes. Although many networks contain this type of temporal information, the majority of …
Graph neural networks for friend ranking in large-scale social platforms
Graph Neural Networks (GNNs) have recently enabled substantial advances in graph
learning. Despite their rich representational capacity, GNNs remain under-explored for large …
learning. Despite their rich representational capacity, GNNs remain under-explored for large …
[PDF][PDF] Temporal network representation learning
JB Lee, G Nguyen, RA Rossi… - arxiv preprint …, 2019 - graphrepresentationlearning.com
Networks evolve continuously over time with the addition, deletion, and changing of links
and nodes. Such temporal networks (or edge streams) consist of a sequence of …
and nodes. Such temporal networks (or edge streams) consist of a sequence of …
Dynamic network embeddings: From random walks to temporal random walks
Networks evolve continuously over time with the addition, deletion, and changing of links
and nodes. Although many networks contain this type of temporal information, the majority of …
and nodes. Although many networks contain this type of temporal information, the majority of …
Chronograph: Enabling temporal graph traversals for efficient information diffusion analysis over time
ChronoGraph is a novel system enabling temporal graph traversals. Compared to snapshot-
oriented systems, this traversal-oriented system is suitable for analyzing information …
oriented systems, this traversal-oriented system is suitable for analyzing information …
Tedic: Neural modeling of behavioral patterns in dynamic social interaction networks
Dynamic social interaction networks are an important abstraction to model time-stamped
social interactions such as eye contact, speaking and listening between people. These …
social interactions such as eye contact, speaking and listening between people. These …
Multi-view change point detection in dynamic networks
Change point detection aims to find the locations of sudden changes in the network
structure, which persist with time. However, most current methods usually focus on how to …
structure, which persist with time. However, most current methods usually focus on how to …
Making graphs compact by lossless contraction
This paper proposes a scheme to reduce big graphs to small graphs. It contracts obsolete
parts, stars, cliques and paths into supernodes. The supernodes carry a synopsis S_Q for …
parts, stars, cliques and paths into supernodes. The supernodes carry a synopsis S_Q for …
Nonuniform timeslicing of dynamic graphs based on visual complexity
Uniform timeslicing of dynamic graphs has been used due to its convenience and uniformity
across the time dimension. However, uniform timeslicing does not take the data set into …
across the time dimension. However, uniform timeslicing does not take the data set into …