243 citations · 462 across the 21 of their papers we have counts for
21 papers
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…
Class-Incremental Learning using Diffusion Model for Distillation and Replay
Quentin Jodelet, Xin Liu, Yin Jun Phua +1
Class-incremental learning aims to learn new classes in an incremental fashion without forgetting the previously learned ones. Several research works have shown how additional data…
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…
Modularity Optimization as a Training Criterion for Graph Neural Networks
Tsuyoshi Murata, Naveed Afzal
Graph convolution is a recent scalable method for performing deep feature learning on attributed graphs by aggregating local node information over multiple layers. Such layers only…
Leaping Through Time with Gradient-based Adaptation for Recommendation
Nuttapong Chairatanakul, Hoang NT, Xin Liu +1
Modern recommender systems are required to adapt to the change in user preferences and item popularity. Such a problem is known as the temporal dynamics problem, and it is one of t…