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Greatest papers with code

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs

CIKM 2020 benedekrozemberczki/karateclub

We present Karate Club a Python framework combining more than 30 state-of-the-art graph mining algorithms which can solve unsupervised machine learning tasks.

COMMUNITY DETECTION GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH MINING NODE CLASSIFICATION

High Quality, Scalable and Parallel Community Detectionfor Large Real Graphs

WWW 2014 benedekrozemberczki/karateclub

However, existing algorithms are, in general, based on complex and expensive computations, making them unsuitable for large graphs with millions of vertices and edges such as those usually found in the real world.

COMMUNITY DETECTION GRAPH MINING

Interactive Text Graph Mining with a Prolog-based Dialog Engine

31 Jul 2020yuce/pyswip

Working on the Prolog facts and their inferred consequences, the dialog engine specializes the text graph with respect to a query and reveals interactively the document's most relevant content elements.

GRAPH MINING

Twitch Gamers: a Dataset for Evaluating Proximity Preserving and Structural Role-based Node Embeddings

8 Jan 2021benedekrozemberczki/datasets

Proximity preserving and structural role-based node embeddings became a prime workhorse of applied graph mining.

GRAPH MINING NODE CLASSIFICATION

Pitfalls of Graph Neural Network Evaluation

14 Nov 2018shchur/gnn-benchmark

We perform a thorough empirical evaluation of four prominent GNN models and show that considering different splits of the data leads to dramatically different rankings of models.

GRAPH MINING NODE CLASSIFICATION

Peregrine: A Pattern-Aware Graph Mining System

6 Apr 2020pdclab/peregrine

General purpose graph mining systems provide a generic runtime to explore subgraph structures of interest with the help of user-defined functions that guide the overall exploration process.

GRAPH MINING DISTRIBUTED, PARALLEL, AND CLUSTER COMPUTING DATABASES D.4; H.3.4; H.2.8

Wikipedia graph mining: dynamic structure of collective memory

1 Oct 2017mizvol/WikiBrain

The model exploits collective effect of the dynamics to discover events.

EVENT EXTRACTION GRAPH MINING TIME SERIES

Persona2vec: A Flexible Multi-role Representations Learning Framework for Graphs

4 Jun 2020jisungyoon/persona2vec

Graph embedding techniques, which learn low-dimensional representations of a graph, are achieving state-of-the-art performance in many graph mining tasks.

GRAPH EMBEDDING GRAPH MINING LINK PREDICTION

Unsupervised Differentiable Multi-aspect Network Embedding

7 Jun 2020pcy1302/asp2vec

To capture the multiple aspects of each node, existing studies mainly rely on offline graph clustering performed prior to the actual embedding, which results in the cluster membership of each node (i. e., node aspect distribution) fixed throughout training of the embedding model.

GRAPH CLUSTERING GRAPH MINING NETWORK EMBEDDING

Deep Network Embedding for Graph Representation Learning in Signed Networks

7 Jan 2019shenxiaocam/Deep-network-embedding-for-graph-representation-learning-in-signed-networks

As an effective approach to solve graph mining problems, network embedding aims to learn a low-dimensional feature vector representation for each node of a given network.

COMMUNITY DETECTION GRAPH MINING GRAPH REPRESENTATION LEARNING LINK SIGN PREDICTION NETWORK EMBEDDING