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[Neural Networks Journal] k-hop Graph Neural Networks

Standard GNNs use a 1-hop aggregation per layer, limiting their ability to capture graph properties. We iteratively extend the aggregation operator of graph neural networks to increase their receptive field. Read more

[ICPR 2020] Hcore-Init: Neural Network Initialization based on Graph Degeneracy

We propose a graph-based initialization of neural networks extending graph degeneracy observations. Such an initialization can encourage neurons that have structural importance in the neural network. Read more

[IJCAI 2020] Coloring Graph Neural Networks for Node Disambiguation

Based on topological criteria and, specifically the separability, we introduce a universal approximation scheme of continuous functions on graphs. It is based on the disambiguation of identical node attributes. Read more

[ICASSP 2021] Ego-based Entropy Measures for Structural Representations on Graphs

Moving beyond local interactions, nodes can share structural similarities, based on their position. We investigate feature augmentation methods of graph neural networks using structural entropy measures. Read more

[ICLR 2021] Learning Parametrised Graph Shift Operators

We propose a parametrised graph shift operator (PGSO) to encode graphs, providing a unified view of common GSOs, and improve GNN performance by including the PGSO into the training in an end-to-end manner. Read more

[ICCV 2021] Graph-based Neural Architecture Search with Operation Embeddings

We propose the replacement of fixed operator encoding in NAS problems with learnable representations in the optimization process. Read more

[ICML 2021] Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks

We derive a theoretical analysis on the Lipschitz continuity of attention and show that enforcing Lipschitz continuity through normalization can significantly improve the performance of deep attention models. Read more

[Neural Networks Journal] Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction

Solving simultaneously link prediction and community detection is important in recommendation systems. Here, we show how we can extend the information that Graph Auto-encoders process towards this direction. Read more

[TBA 2022] Graph Ordering Attention Networks

Based on connections with the Partial Information Decomposition framework, we introduce a novel GNN layer, namely the Graph Ordering Attention (GOAT) that imposes neighborhood orderings according to the attention coefficients. Read more

[PAMI] Permute Me Softly: Learning Soft Permutations for Graph Representations

We study how we can approximate graph distances by aligning adjacency matrices in a corpus of graphs. In order to allow the differentiable optimization, we suggest the utilization of soft permutation matrices. Read more