Research map / Graph learning / Knowledge and temporal graphs
Differentiable Cluster Discovery in Temporal Graphs
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Proposes a differentiable approach to temporal graph clustering that addresses unstable optimization caused by heuristically initialized cluster assignment targets that conflict with evolving temporal representations.
Problem
Temporal graph clustering methods rely on heuristically initialized cluster assignment distributions that ignore graph dynamics, causing conflicting targets, oscillatory gradients, and unstable convergence.
Approach
Motivated by the need for differentiable cluster discovery, the method learns cluster assignments in a differentiable manner rather than fixing a heuristic target distribution; details are not available in the provided abstract.
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