Jul 27, 2024 · A link prediction framework is presented to predict missing links using parameterized influence regions of nodes and their contribution in community partitions.

Nov 30, 2023 · Our aim is to predict the link probability of a given node with respect to all the other nodes of the graph. Given a snapshot of the network at time t, we seek ...

Sep 23, 2024 · We present an attempt at unifying points of view and analyses of these objects coming from the social sciences, statistics, probability and physics communities.

Feb 9, 2024 · Directed connectivity inference has become a cornerstone in neuroscience to analyze multivariate data from neuroimaging and electrophysiological techniques.

Feb 19, 2024 · This paper concerns the analysis of network data when unobserved node-specific heterogeneity is present. We postulate a weighted version of the classic ...

Sep 22, 2024 · In this paper, the DTCN model is constructed to ensure that the prediction of the AUV motion state is more accurate, and at the same time, to reduce the ...

Jan 16, 2024 · Temporal link prediction as a task in the field of dynamic graph learning can be done by modeling the underlying probabilistic dependencies between nodes and ...

Oct 31, 2023 · In this work, we propose a data-driven method that can adapt to the specific network domain, and be used to detect distribution changes with no delay and in an ...

Jun 12, 2024 · Predicting individual behavior from brain functional connectivity (FC) patterns can contribute to our understanding of human brain functioning.

Sep 19, 2024 · In this study, we introduce a self-supervised method for learning representations of temporal networks and employ these representations in the dynamic link ...