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Graph Embedding

25 papers with code · Graphs

Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.

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Greatest papers with code

Poincaré Embeddings for Learning Hierarchical Representations

NeurIPS 2017 facebookresearch/poincare-embeddings

Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, while complex symbolic datasets often exhibit a latent hierarchical structure, state-of-the-art methods typically learn embeddings in Euclidean vector spaces, which do not account for this property.

GRAPH EMBEDDING

LINE: Large-scale Information Network Embedding

12 Mar 2015tangjianpku/LINE

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes.

GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

Graph Embedding Techniques, Applications, and Performance: A Survey

8 May 2017palash1992/GEM

Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications. Analyzing them yields insight into the structure of society, language, and different patterns of communication.

GRAPH EMBEDDING

DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning

EMNLP 2017 xwhan/DeepPath

We study the problem of learning to reason in large scale knowledge graphs (KGs). More specifically, we describe a novel reinforcement learning framework for learning multi-hop relational paths: we use a policy-based agent with continuous states based on knowledge graph embeddings, which reasons in a KG vector space by sampling the most promising relation to extend its path.

KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

HLT 2018 cai-lw/KBGAN

Because knowledge graphs typically only contain positive facts, sampling useful negative training examples is a non-trivial task. This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS LINK PREDICTION

Learning Combinatorial Optimization Algorithms over Graphs

NeurIPS 2017 Hanjun-Dai/graph_comb_opt

The design of good heuristics or approximation algorithms for NP-hard combinatorial optimization problems often requires significant specialized knowledge and trial-and-error. Can we automate this challenging, tedious process, and learn the algorithms instead?

COMBINATORIAL OPTIMIZATION GRAPH EMBEDDING

GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders

ICLR 2018 snap-stanford/GraphRNN

Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks, which is in contrast with advances in generative models for images and text.

GRAPH EMBEDDING

Multi-Task Graph Autoencoders

7 Nov 2018vuptran/graph-representation-learning

We examine two fundamental tasks associated with graph representation learning: link prediction and node classification. We present a new autoencoder architecture capable of learning a joint representation of local graph structure and available node features for the simultaneous multi-task learning of unsupervised link prediction and semi-supervised node classification.

GRAPH EMBEDDING GRAPH REPRESENTATION LEARNING LINK PREDICTION MULTI-TASK LEARNING NODE CLASSIFICATION

DynamicGEM: A Library for Dynamic Graph Embedding Methods

26 Nov 2018palash1992/DynamicGEM

DynamicGEM is an open-source Python library for learning node representations of dynamic graphs. It consists of state-of-the-art algorithms for defining embeddings of nodes whose connections evolve over time.

GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION

Adversarially Regularized Graph Autoencoder for Graph Embedding

13 Feb 2018Ruiqi-Hu/ARGA

Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which often results in inferior embedding in real-world graph data.

GRAPH CLUSTERING GRAPH EMBEDDING LINK PREDICTION