All Projects → snash4 → Gat2vec

snash4 / Gat2vec

Licence: gpl-2.0
representation learning on attributed graphs

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Gat2vec

Logicflow
A front-end framework for process visualization.
Stars: ✭ 973 (+1927.08%)
Mutual labels:  graph
Word2vec Win32
A word2vec port for Windows.
Stars: ✭ 41 (-14.58%)
Mutual labels:  representation-learning
Neo4j Helm
Helm Charts for running Neo4j on Kubernetes
Stars: ✭ 43 (-10.42%)
Mutual labels:  graph
Dgcnn
A PyTorch implementation of DGCNN based on AAAI 2018 paper "An End-to-End Deep Learning Architecture for Graph Classification"
Stars: ✭ 37 (-22.92%)
Mutual labels:  graph
Ingraph
Incremental view maintenance for openCypher graph queries.
Stars: ✭ 40 (-16.67%)
Mutual labels:  graph
G6
♾ A Graph Visualization Framework in JavaScript
Stars: ✭ 8,490 (+17587.5%)
Mutual labels:  graph
Cracking The Coding Interview
Solutions for Cracking the Coding Interview - 6th Edition
Stars: ✭ 35 (-27.08%)
Mutual labels:  graph
Indradb
A graph database written in rust
Stars: ✭ 1,035 (+2056.25%)
Mutual labels:  graph
Qualia2.0
Qualia is a deep learning framework deeply integrated with automatic differentiation and dynamic graphing with CUDA acceleration. Qualia was built from scratch.
Stars: ✭ 41 (-14.58%)
Mutual labels:  graph
Algorithms
Solved algorithms and data structures problems in many languages
Stars: ✭ 1,021 (+2027.08%)
Mutual labels:  graph
Graphrole
Automatic feature extraction and node role assignment for transfer learning on graphs (ReFeX & RolX)
Stars: ✭ 38 (-20.83%)
Mutual labels:  graph
Scala Plotly Client
Visualise your data from Scala using Plotly
Stars: ✭ 39 (-18.75%)
Mutual labels:  graph
Compress
Compressing Representations for Self-Supervised Learning
Stars: ✭ 43 (-10.42%)
Mutual labels:  representation-learning
Multi Robot Path Planning On Graphs
Multi-Robot Path Planning on Graphs Solution by A* algorithm
Stars: ✭ 36 (-25%)
Mutual labels:  graph
Awesome Graph Representation Learning
A curated list of awesome graph representation learning.
Stars: ✭ 44 (-8.33%)
Mutual labels:  graph
Ig Follow Count
📈 A simple Instagram analytics tool that continuously logs and graphs your follower count.
Stars: ✭ 35 (-27.08%)
Mutual labels:  graph
Resonance
◾️Resonance | 5kb React animation library
Stars: ✭ 1,011 (+2006.25%)
Mutual labels:  graph
Pyrwr
Python Implementation for Random Walk with Restart (RWR)
Stars: ✭ 48 (+0%)
Mutual labels:  graph
Videocontext
An experimental HTML5 & WebGL video composition and rendering API.
Stars: ✭ 1,035 (+2056.25%)
Mutual labels:  graph
Simpleweightedgraphs.jl
Simple weighted graphs. Requires LightGraphs.jl.
Stars: ✭ 43 (-10.42%)
Mutual labels:  graph

GAT2VEC

Representation Learning for Attributed Graphs is a framework for learning a represenation using multiple sources of information.

DIRECTORIES:

  1. src :- has the source code for GAT2VEC, and evaluation
  2. data :- it contains the input networks in respective directories, along with labels for classification task the files adjedges.txt, labels.txt, and docs.txt are the orignal files of the datasets. we preprocess and generate the files for GAT2VEC processing and to bring the uniformity in the for vertex id's We uniformly start vertex id's from 1.
  3. embeddings: the embeddings learned are stored in this directory

DATA FORMAT GAT2VEC reads network in adjacency list. It needs two types of files:

  1. _graph.adjlist : This adjacency list represents the structural graph (directed or undirected).
  2. _na.adjlist: This adjacency list is an undirected bipartite graph. The structural vertices are numbered from to 1 to num. of structural nodes, and the attribute vertices are numbered after structural vertices. This bipartite graph doesn't contain labels as attributes.

The file <network_name>_label_10_na.adjlist is a bipartite graph in which labels of 10% of nodes are incorporated as attributes.

USAGE: To learn a representation without using label information.

1. #python __main__.py --data M10

To learn a representation using labels.

2. #python __main__.py --data M10 --label True

To learn a representation only using bi-partite graph

3. #python __main__.py --data M10 --algo g2vbip

Paper

Please cite our paper if you find the code useful for your research.

@inproceedings{gat2vec,
  booktitle="Journal of Computing",
	author = {Sheikh, Nasrullah and Kefato, Zekarias T. and Montresor, Alberto},
	title = "gat2vec: Representation Learning for Attributed Graphs",
	month = May,
	year = 2018,
	type = {JOURNAL},
	pdfurl = {https://link.springer.com/content/pdf/10.1007/s00607-018-0622-9.pdf},	
	publisher = {Springer},
}

PS: The pre-processing code for generating structural and bipartite graph will be uploaded soon My python code has influence of Java :)

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].