23 citations · 47 across the 7 of their papers we have counts for
4 papers · 1 filter
Modularity-based selection of the number of slices in temporal network clustering
Patrik Seiron, Axel Lindegren, Matteo Magnani +3
A popular way to cluster a temporal network is to transform it into a sequence of networks, also called slices, where each slice corresponds to a time interval and contains the ver…
Influence maximization on temporal networks: a review
Eric Yanchenko, Tsuyoshi Murata, Petter Holme
Influence maximization (IM) is an important topic in network science where a small seed set is chosen to maximize the spread of influence on a network. Recently, this problem has a…
Link prediction for ex ante influence maximization on temporal networks
Eric Yanchenko, Tsuyoshi Murata, Petter Holme
Influence maximization (IM) is the task of finding the most important nodes in order to maximize the spread of influence or information on a network. This task is typically studied…
Extending modularity by capturing the similarity attraction feature in the null model
Xin Liu, Tsuyoshi Murata, Ken Wakita
Modularity is a widely used measure for evaluating community structure in networks. The definition of modularity involves a comparison of within-community edges in the observed net…