Dynamic Representation Learning with Temporal Point Processes for
Higher-Order Interaction Forecasting
- URL: http://arxiv.org/abs/2112.10154v4
- Date: Sat, 1 Apr 2023 07:51:06 GMT
- Title: Dynamic Representation Learning with Temporal Point Processes for
Higher-Order Interaction Forecasting
- Authors: Tony Gracious, Ambedkar Dukkipati
- Abstract summary: This paper proposes a temporal point process model for hyperedge prediction to address these problems.
As far as our knowledge, this is the first work that uses the temporal point process to forecast hyperedges in dynamic networks.
- Score: 8.680676599607123
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The explosion of digital information and the growing involvement of people in
social networks led to enormous research activity to develop methods that can
extract meaningful information from interaction data. Commonly, interactions
are represented by edges in a network or a graph, which implicitly assumes that
the interactions are pairwise and static. However, real-world interactions
deviate from these assumptions: (i) interactions can be multi-way, involving
more than two nodes or individuals (e.g., family relationships, protein
interactions), and (ii) interactions can change over a period of time (e.g.,
change of opinions and friendship status). While pairwise interactions have
been studied in a dynamic network setting and multi-way interactions have been
studied using hypergraphs in static networks, there exists no method, at
present, that can predict multi-way interactions or hyperedges in dynamic
settings. Existing related methods cannot answer temporal queries like what
type of interaction will occur next and when it will occur. This paper proposes
a temporal point process model for hyperedge prediction to address these
problems. Our proposed model uses dynamic representation learning techniques
for nodes in a neural point process framework to forecast hyperedges. We
present several experimental results and set benchmark results. As far as our
knowledge, this is the first work that uses the temporal point process to
forecast hyperedges in dynamic networks.
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