Analyzing the Evolution of Graphs and Texts
Not on the map. Why?
Abstract (arXiv)
With the recent advance of representation learning algorithms on graphs (e.g., DeepWalk/GraphSage) and natural languages (e.g., Word2Vec/BERT) , the state-of-the art models can even achieve human-level performance over many downstream tasks, particularly for the task of node and sentence classification. However, most algorithms focus on large-scale models for static graphs and text corpus without considering the inherent dynamic characteristics or discovering the reasons behind the changes. This dissertation aims to efficiently model the dynamics in graphs (such as social networks and citation graphs) and understand the changes in texts (specifically news titles and personal biographies). To achieve this goal, we utilize the renowned Personalized PageRank algorithm to create effective dynamic network embeddings for evolving graphs. Our proposed approaches significantly improve the running time and accuracy for both detecting network abnormal intruders and discovering entity meaning shifts over large-scale dynamic graphs. For text changes, we analyze the post-publication changes in news titles to understand the intents behind the edits and discuss the potential impact of titles changes from information integrity perspective. Moreover, we investigate self-presented occupational identities in Twitter users' biographies over five years, investigating job prestige and demographics effects in how people disclose jobs, quantifying over-represented jobs and their transitions over time.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 207 references that are in the map. First posted on arXiv 9 Nov 2024.
Builds on
27 of 207 references.
References · 27
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
- 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 993
- 2016Bag of Tricks for Efficient Text Classificationcited by 876
- 2020Stanza: A Python Natural Language Processing Toolkit for Many Human Languagescited by 723
- 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 595
- 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 530
- 2020Inductive Representation Learning on Temporal Graphscited by 462
- 2017Representation Learning on Graphs: Methods and Applicationscited by 457
- 2016FastText.zip: Compressing text classification modelscited by 335
- 2019On the Use of ArXiv as a Datasetcited by 103
- 2018DynGEM: Deep Embedding Method for Dynamic Graphscited by 102
15 more
- 2022How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervisioncited by 102
- 2018Learning to Represent Editscited by 34
- 2018False Information on Web and Social Media: A Surveycited by 33
- 2018Predicting Race and Ethnicity From the Sequence of Characters in a Namecited by 26
- 2017Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergencecited by 17
- 2016Approximate Personalized PageRank on Dynamic Graphscited by 11
- 2020InstantEmbedding: Efficient Local Node Representationscited by 9
- 2021Sketch-Based Anomaly Detection in Streaming Graphscited by 9
- 2020Using Noisy Self-Reports to Predict Twitter User Demographicscited by 8
- 2022Provable Fairness for Neural Network Models using Formal Verificationcited by 6
- 2020Variational Inference for Learning Representations of Natural Language Editscited by 5
- 2024Iterative Methods via Locally Evolving Set Processcited by 5
- 2022Knowledge Authoring with Factual Englishcited by 3
- 2022Hierarchies over Vector Space: Orienting Word and Graph Embeddingscited by 3
- 2022Edge-based Local Push for Personalized PageRankcited by 2
Built on by
Not cited yet.