Predicting Influential Higher-Order Patterns in Temporal Network Data
- URL: http://arxiv.org/abs/2107.12100v1
- Date: Mon, 26 Jul 2021 10:44:46 GMT
- Title: Predicting Influential Higher-Order Patterns in Temporal Network Data
- Authors: Christoph Gote and Vincenzo Perri and Ingo Scholtes
- Abstract summary: We propose eight centrality measures based on MOGen, a multi-order generative model that accounts for all paths up to a maximum distance but disregards paths at higher distances.
We show that MOGen consistently outperforms both the network model and path-based prediction.
- Score: 2.5782420501870287
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Networks are frequently used to model complex systems comprised of
interacting elements. While links capture the topology of direct interactions,
the true complexity of many systems originates from higher-order patterns in
paths by which nodes can indirectly influence each other. Path data,
representing ordered sequences of consecutive direct interactions, can be used
to model these patterns. However, to avoid overfitting, such models should only
consider those higher-order patterns for which the data provide sufficient
statistical evidence. On the other hand, we hypothesise that network models,
which capture only direct interactions, underfit higher-order patterns present
in data. Consequently, both approaches are likely to misidentify influential
nodes in complex networks. We contribute to this issue by proposing eight
centrality measures based on MOGen, a multi-order generative model that
accounts for all paths up to a maximum distance but disregards paths at higher
distances. We compare MOGen-based centralities to equivalent measures for
network models and path data in a prediction experiment where we aim to
identify influential nodes in out-of-sample data. Our results show strong
evidence supporting our hypothesis. MOGen consistently outperforms both the
network model and path-based prediction. We further show that the performance
difference between MOGen and the path-based approach disappears if we have
sufficient observations, confirming that the error is due to overfitting.
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