tNodeEmbed: Node Embeddings over Temporal Graphs | ML with Graphs (Research Paper Walkthrough)

#machinelearning #graphs #embeddings
Realworld use-cases of graphs such as social media network, etc are temporal in nature. Classic node embedding techniques such as DeepWalk, Node2Vec, etc work well on static graphs and tend to miss out rich information and patterns that exists in temporally evolving graphs. This paper talks about modeling such graphs using LSTMs. Watch video to know more :)

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⏩ 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.

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⏩ OUTLINE:
0:00 - Background
1:11 - Mito Promotion
1:51 - Abstract
3:30 - Feature Learning Framework (Loss Function, Node Initialization, Temporal Alignment)
9:51 - Initialization using Static Node Embeddings (Node2Vec Overview)
13:22 - Node Embedding over Time
15:20 - Conclusion and My thoughts

⏩ Paper Title: Node Embedding over Temporal Graphs
⏩ Paper: https://arxiv.org/abs/1903.08889
⏩ Code: https://github.com/urielsinger/tNodeEmbed
⏩ Author: Uriel Singer, Ido Guy, Kira Radinsky
⏩ Organisation: Technion, Israel Institute of Technology, eBay Research

⏩ IMPORTANT LINKS
Full Playlist on BERT usecases in NLP: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f
Full Playlist on Text Data Augmentation Techniques: https://www.youtube.com/watch?v=9O9scQb4sNo&list=PLsAqq9lZFOtUg63g_95OuV-R2GhV1UiIZ
Full Playlist on Text Summarization: https://www.youtube.com/watch?v=kC5kP1dPAzc&list=PLsAqq9lZFOtV8jYq3JlkqPQUN5QxcWq0f
Full Playlist on Machine Learning with Graphs: https://www.youtube.com/watch?v=-uJL_ANy1jc&list=PLsAqq9lZFOtU7tT6mDXX_fhv1R1-jGiYf
Full Playlist on Evaluating NLG Systems: https://www.youtube.com/watch?v=-CIlz-5um7U&list=PLsAqq9lZFOtXlzg5RNyV00ueE89PwnCbu

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