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Latest papers with code

User Preference-aware Fake News Detection

25 Apr 2021safe-graph/GNN-FakeNews

The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored.

FACT CHECKING FAKE NEWS DETECTION GRAPH CLASSIFICATION MISINFORMATION

8
25 Apr 2021

Quadratic GCN for Graph Classification

14 Apr 2021Unknown-Data/QGCN

We here propose a novel solution combining GCN, methods from knowledge graphs, and a new self-regularized activation function to significantly improve the accuracy of the GCN based GCT.

CLASSIFICATION GRAPH CLASSIFICATION KNOWLEDGE GRAPHS

1
14 Apr 2021

Smart Vectorizations for Single and Multiparameter Persistence

10 Apr 2021cakcora/PeaceCorps

We derive theoretical guarantees on the stability of the new saw and multi-persistence grid functions and illustrate their applicability for graph classification tasks.

ANOMALY DETECTION GRAPH CLASSIFICATION TOPOLOGICAL DATA ANALYSIS

0
10 Apr 2021

Parameterized Hypercomplex Graph Neural Networks for Graph Classification

30 Mar 2021bayer-science-for-a-better-life/phc-gnn

Despite recent advances in representation learning in hypercomplex (HC) space, this subject is still vastly unexplored in the context of graphs.

CLASSIFICATION GRAPH CLASSIFICATION REPRESENTATION LEARNING

12
30 Mar 2021
674
19 Mar 2021

Size-Invariant Graph Representations for Graph Classification Extrapolations

8 Mar 2021PurdueMINDS/size-invariant-GNNs

In general, graph representation learning methods assume that the test and train data come from the same distribution.

CLASSIFICATION GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING

3
08 Mar 2021

Structure-Enhanced Meta-Learning For Few-Shot Graph Classification

5 Mar 2021jiangshunyu/SMF-GIN

Graph classification is a highly impactful task that plays a crucial role in a myriad of real-world applications such as molecular property prediction and protein function prediction. Aiming to handle the new classes with limited labeled graphs, few-shot graph classification has become a bridge of existing graph classification solutions and practical usage. This work explores the potential of metric-based meta-learning for solving few-shot graph classification. We highlight the importance of considering structural characteristics in the solution and propose a novel framework which explicitly considers global structure and local structure of the input graph.

CLASSIFICATION GRAPH CLASSIFICATION META-LEARNING MOLECULAR PROPERTY PREDICTION PROTEIN FUNCTION PREDICTION

0
05 Mar 2021

CogDL: An Extensive Toolkit for Deep Learning on Graphs

1 Mar 2021THUDM/cogdl

Most of the graph embedding methods learn node-level or graph-level representations in an unsupervised way and preserves the graph properties such as structural information, while graph neural networks capture node features and work in semi-supervised or self-supervised settings.

GRAPH CLASSIFICATION GRAPH EMBEDDING GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION RECOMMENDATION SYSTEMS

654
01 Mar 2021

Accurate Learning of Graph Representations with Graph Multiset Pooling

ICLR 2021 JinheonBaek/GMT

Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks.

GRAPH CLASSIFICATION GRAPH CLUSTERING GRAPH EMBEDDING GRAPH GENERATION GRAPH LEARNING GRAPH RECONSTRUCTION NODE CLUSTERING

17
23 Feb 2021