23 papers with code ·
Graphs

ICLR 2019 • benedekrozemberczki/CapsGNN •

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.

SOTA for Graph Classification on PROTEINS

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.

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

SOTA for Text Classification on R8

GRAPH CLASSIFICATION IMAGE CLASSIFICATION RELATION EXTRACTION SENTIMENT ANALYSIS TEXT CLASSIFICATION

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

GRAPH CLASSIFICATION LINK PREDICTION REPRESENTATION LEARNING

edouardpineau/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.

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

ICLR 2019 • zhliping/Deep-Learning •

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

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.

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.

KDD 2018 • benedekrozemberczki/GAM •

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