  Contagions (e.g. virus, gossip) spread over the nodes in propagation graphs.
We can use the temporal and textual data of the nodes to compute the edge
weights and then generate subgraphs with highly relevant nodes. This is
beneficial to many applications. Yet, challenges abound. First, the propagation
pattern between each pair of nodes may change by time. Second, not always the
same contagion propagates. Hence, the state-of-the-art text mining approaches
including topic-modeling cannot effectively compute the edge weights. Third,
since the propagation is affected by time, the word-word co-occurrence patterns
may differ in various temporal dimensions, that can decrease the effectiveness
of word embedding approaches. We argue that multi-aspect temporal dimensions
(hour, day, etc) should be considered to better calculate the correlation
weights between the nodes. In this work, we devise a novel framework that on
the one hand, integrates a neural network based time-aware word embedding
component to construct the word vectors through multiple temporal facets, and
on the other hand, uses a temporal generative model to compute the weights.
Subsequently, we propose a Max-Heap Graph cutting algorithm to generate
subgraphs. We validate our model through comprehensive experiments on
real-world datasets. The results show that our model can retrieve the subgraphs
more effective than other rivals and the temporal dynamics should be noticed
both in word embedding and propagation processes.
