Abstract
In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph’s
nodes and edges over time and incorporates this dynamics in a temporal node embedding framework
for different graph prediction tasks. We present a
joint loss function that creates a temporal embedding of a node by learning to combine its historical temporal embeddings, such that it optimizes
per given task (e.g., link prediction). The algorithm
is initialized using static node embeddings, which
are then aligned over the representations of a node
at different time points, and eventually adapted for
the given task in a joint optimization. We evaluate the effectiveness of our approach over a variety of temporal graphs for the two fundamental
tasks of temporal link prediction and multi-label
node classification, comparing to competitive baselines and algorithmic alternatives. Our algorithm
shows performance improvements across many of
the datasets and baselines and is found particularly
effective for graphs that are less cohesive, with a
lower clustering coefficient