Activity-Driven Influence Maximization in Social Networks

Rohit Kumar, Muhammad Aamir Saleem, Toon Calders, Xike Xie, Torben Bach Pedersen

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

1 Citationer (Scopus)


Interaction networks consist of a static graph with a timestamped list of edges over which interaction took place. Examples of interaction networks are social networks whose users interact with each other through messages or location-based social networks where people interact by checking in to locations. Previous work on finding influential nodes in such networks mainly concentrate on the static structure imposed by the interactions or are based on fixed models for which parameters are learned using the interactions. In two recent works, however, we proposed an alternative activity data-driven approach based on the identification of influence propagation patterns. In the first work, we identify so-called information-channels to model potential pathways for information spread, while the second work exploits how users in a location-based social network check in to locations in order to identify influential locations. To make our algorithms scalable, approximate versions based on sketching techniques from the data streams domain have been developed. Experiments show that in this way it is possible to efficiently find good seed sets for influence propagation in social networks.
TitelMachine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III
ISBN (Trykt)978-3-319-71272-7
ISBN (Elektronisk)978-3-319-71273-4
StatusUdgivet - 2017
BegivenhedJoint European Conference on Machine Learning and Knowledge Discovery in Databases - Skopje, Makedonien
Varighed: 18 sep. 201722 sep. 2017


KonferenceJoint European Conference on Machine Learning and Knowledge Discovery in Databases
NavnLecture Notes in Computer Science

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