NS-CUK Seminar: V.T.Hoang, Review on "Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily", TNNLS 2023

1. Van Thuy Hoang
Dept. of Artificial Intelligence,
The Catholic University of Korea
hoangvanthuy90@gmail.com

2. 2
The inter-class edges
 The identifiability of neighbors. Nodes with the same colors share
the same class labels.
 The above neighbor distribution for the blue class is random, and
the bottom is not random.
 the inter-class edges may be beneficial to improve the node
classification task if their neighbor distribution is identifiable instead
of random.

3. 3
Contributions
 A new perspective of heterophily with neighbor identifiability and quantify it
with the metric inspired by the von Neumann entropy.
 Propose CAGNNs as a general framework to improve classical GNNs by
learning the neighbor effect for each node
 Experiments on nine well-known benchmark datasets to verify the
effectiveness, interpretability, and robustness of CAGNNs.

4. 4
Graph Neural Networks
 most GNNs assume the local Markov property on node features, i.e.,
for each node

5. 5
Homophily/Heterophily Metrics on Grap
 The homophily ratio h aims to measure the overall homophily level
in a graph

6. 6
Class-level von Neumann entropy
 measures the information of neighbors’ label distribution matrix.
 This metric ranges from [0, 1] and can quantify the identifiability of
neighbors for a specific class (a lower number indicates higher
identifiability of neighbors).

7. 7
PROPOSED METHOD
 the Conv-Agnostic GNN framework (CAGNNs) to improve traditional
GNNs performance by adaptively learning the node-level neighbor
effect.

8. 8
PROPOSED METHOD
 Encoder: We use a linear layer as the encoder to transform the node
features X.

9. 9
PROPOSED METHOD
 Graph Convolution (GC): framework is ConvAgnostic, in this part, any
standard graph convolution layers (e.g., GCN, GAT, and GIN) can be
applied to aggregate each node’s neighborhood information to
update the aggregation representation H

10. 10
PROPOSED METHOD
 Mixer:
 the goal of the mixer function is to evaluate the neighbor effect of
each node and then to selectively incorporate the neighbors’
information.

11. 11
PROPOSED METHOD
 Decoder: is to produce the final prediction Z for classification.

12. 12
Experiemnts
 Datasets:
 (Citeseer, Pubmed, and Cora)
 Six heterophily datasets (Texas, Wisconsin, Actor, Squirrel,
Chameleon, and Cornell).

13. 13
Experiemnnts
 The average performance (test accuracy) over all datasets for the
ablation study of different types of Mixers and Normalization.