Browse > Graphs > Graph Classification

# Graph Classification Edit

23 papers with code · Graphs

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# Capsule Graph Neural Network

The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance.

376
01 May 2019

# Capsule Neural Networks for Graph Classification using Explicit Tensorial Graph Representations

22 Feb 2019BraintreeLtd/PatchyCapsules

Building on prior work combining explicit tensor representations with a standard image-based classifier, we propose a model to perform graph classification by extracting fixed size tensorial information from each graph in a given set, and using a Capsule Network to perform classification.

33
22 Feb 2019

# Simplifying Graph Convolutional Networks

19 Feb 2019Tiiiger/SGC

Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.

175
19 Feb 2019

# A simple yet effective baseline for non-attribute graph classification

8 Nov 2018Chen-Cai-OSU/LDP

We test our baseline representation for the graph classification task on a range of graph datasets.

1
08 Nov 2018

# A Simple Baseline Algorithm for Graph Classification

Graph classification has recently received a lot of attention from various fields of machine learning e. g. kernel methods, sequential modeling or graph embedding.

6
22 Oct 2018

# Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks

4 Oct 2018k-gnn/k-gnn

We show that GNNs have the same expressiveness as the $1$-WL in terms of distinguishing non-isomorphic (sub-)graphs.

14
04 Oct 2018

# How Powerful are Graph Neural Networks?

Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.

2
01 Oct 2018

# Graph Edit Distance Computation via Graph Neural Networks

16 Aug 2018benedekrozemberczki/SimGNN

Graph similarity/distance computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other applications, but very costly to compute in practice.

62
16 Aug 2018

# Attention Models in Graphs: A Survey

20 Jul 2018zhliping/Deep-Learning

However, in the real-world, graphs can be both large - with many complex patterns - and noisy which can pose a problem for effective graph mining.

2
20 Jul 2018

# Graph Classification using Structural Attention

Graph classification is a problem with practical applications in many different domains.

74
19 Jul 2018