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Found 16 papers, 9 papers with code
Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation
Within our methodology, in order to quantify the sustainability awareness of these AI models, we propose a definition of the code's "green capacity", based on certain sustainability metrics.
Learn2Extend: Extending sequences by retaining their statistical properties with mixture models
Leveraging advancements in deep learning applied to point processes, this paper explores the use of an auto-regressive \textit{Sequence Extension Mixture Model} (SEMM) for extending finite sequences, by estimating directly the conditional density, instead of the intensity function.
GNNDelete: A General Strategy for Unlearning in Graph Neural Networks
Deleted Edge Consistency ensures that the influence of deleted elements is removed from both model weights and neighboring representations, while Neighborhood Influence guarantees that the remaining model knowledge is preserved after deletion.
New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction
It is still unclear to what extent one can improve CD with GAE and VGAE, especially in the absence of node features.
Multimodal learning with graphs
Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics.
Graph Ordering Attention Networks
The GOAT model demonstrates its increased performance in modeling graph metrics that capture complex information, such as the betweenness centrality and the effective size of a node.
Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction.
Permute Me Softly: Learning Soft Permutations for Graph Representations
In this paper, we propose a new graph neural network model, so-called $\pi$-GNN which learns a "soft" permutation (i. e., doubly stochastic) matrix for each graph, and thus projects all graphs into a common vector space.
Graph-based Neural Architecture Search with Operation Embeddings
Neural Architecture Search (NAS) has recently gained increased attention, as a class of approaches that automatically searches in an input space of network architectures.
Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks
To address this issue, we derive a theoretical analysis of the Lipschitz continuity of attention modules and introduce LipschitzNorm, a simple and parameter-free normalization for self-attention mechanisms that enforces the model to be Lipschitz continuous.
Ego-based Entropy Measures for Structural Representations on Graphs
Machine learning on graph-structured data has attracted high research interest due to the emergence of Graph Neural Networks (GNNs).
Learning Parametrised Graph Shift Operators
In many domains data is currently represented as graphs and therefore, the graph representation of this data becomes increasingly important in machine learning.
Hcore-Init: Neural Network Initialization based on Graph Degeneracy
As a multipartite graph is a combination of bipartite graphs, that are in turn the incidence graphs of hypergraphs, we design k-hypercore decomposition, the hypergraph analogue of k-core degeneracy.
Ego-based Entropy Measures for Structural Representations
Moreover, on graph classification tasks, we suggest the utilization of the generated structural embeddings for the transformation of an attributed graph structure into a set of augmented node attributes.
Coloring graph neural networks for node disambiguation
In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks(MPNNs).
k-hop Graph Neural Networks
We show that the proposed architecture can identify fundamental graph properties.
