TEAGS: Time-aware Text Embedding Approach to Generate Subgraphs
Saeid Hosseini, Saeed Najafipour, Ngai-Man Cheung, Hongzhi Yin,, Mohammad Reza Kangavari, and Xiaofang Zhou

TL;DR
This paper introduces TEAGS, a novel time-aware text embedding framework that incorporates multi-aspect temporal data to generate more relevant subgraphs in propagation networks, outperforming existing methods.
Contribution
The paper presents a new neural network-based time-aware word embedding approach combined with a temporal generative model for subgraph extraction, considering multi-aspect temporal dimensions.
Findings
TEAGS outperforms existing methods in subgraph retrieval accuracy.
Incorporating multi-aspect temporal data improves embedding quality.
Temporal dynamics are crucial for effective propagation modeling.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Peer-to-Peer Network Technologies
