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Network Embedding

62 papers with code · Methodology

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Fast Network Embedding Enhancement via High Order Proximity Approximation

‏‏‎ ‎ 2020 benedekrozemberczki/karateclub

Many Network Representation Learning (NRL) methods have been proposed to learn vector representations for vertices in a network recently.

DIMENSIONALITY REDUCTION LINK PREDICTION MULTI-LABEL CLASSIFICATION NETWORK EMBEDDING

753
13 May 2020

New Datasets and a Benchmark of Document Network Embedding Methods for Scientific Expert Finding

7 Apr 2020brochier/expert_finding

In this direction, document network embedding methods seem to be an ideal choice for building representations of the scientific literature.

NETWORK EMBEDDING

0
07 Apr 2020

Heterogeneous Network Representation Learning: Survey, Benchmark, Evaluation, and Beyond

1 Apr 2020yangji9181/HNE

Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic superclass of traditional homogeneous networks (graphs).

NETWORK EMBEDDING

34
01 Apr 2020

Network Embedding with Completely-imbalanced Labels

IEEE Transactions on Knowledge and Data Engineering 2020 zhengwang100/RECT

Network embedding, aiming to project a network into a low-dimensional space, is increasingly becoming a focus of network research.

NETWORK EMBEDDING

7
03 Feb 2020

Fast Sequence-Based Embedding with Diffusion Graphs

21 Jan 2020benedekrozemberczki/karateclub

A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes.

COMMUNITY DETECTION GRAPH EMBEDDING NETWORK EMBEDDING

753
21 Jan 2020

Inductive Document Network Embedding with Topic-Word Attention

10 Jan 2020brochier/idne

We train these word and topic vectors through our general model, Inductive Document Network Embedding (IDNE), by leveraging the connections in the document network.

NETWORK EMBEDDING

13
10 Jan 2020

A Non-negative Symmetric Encoder-Decoder Approach for Community Detection

CIKM 2019 benedekrozemberczki/karateclub

Latent factor models for community detection aim to find a distributed and generally low-dimensional representation, or coding, that captures the structural regularity of network and reflects the community membership of nodes.

COMMUNITY DETECTION GRAPH CLUSTERING NETWORK EMBEDDING NODE CLASSIFICATION

753
24 Dec 2019

JNET: Learning User Representations via Joint Network Embedding and Topic Embedding

1 Dec 2019Linda-sunshine/JNET

Inspired by the concept of user schema in social psychology, we take a new perspective to perform user representation learning by constructing a shared latent space to capture the dependency among different modalities of user-generated data.

LINK PREDICTION NETWORK EMBEDDING

6
01 Dec 2019

Improving Textual Network Learning with Variational Homophilic Embeddings

NeurIPS 2019 Wenlin-Wang/VHE19

This paper considers a novel variational formulation of network embeddings, with special focus on textual networks.

NETWORK EMBEDDING

2
01 Dec 2019

Unsupervised Attributed Multiplex Network Embedding

15 Nov 2019pcy1302/DMGI

Even for those that consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph.

NETWORK EMBEDDING

44
15 Nov 2019