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Found 7 papers, 4 papers with code
Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings
We present a novel edge-level ego-network encoding for learning on graphs that can boost Message Passing Graph Neural Networks (MP-GNNs) by providing additional node and edge features or extending message-passing formats.
Beyond 1-WL with Local Ego-Network Encodings
Identifying similar network structures is key to capture graph isomorphisms and learn representations that exploit structural information encoded in graph data.
Large scale analysis of gender bias and sexism in song lyrics
We employ Natural Language Processing techniques to analyse 377808 English song lyrics from the "Two Million Song Database" corpus, focusing on the expression of sexism across five decades (1960-2010) and the measurement of gender biases.
Uncovering the Limits of Text-based Emotion Detection
Identifying emotions from text is crucial for a variety of real world tasks.
Inductive Graph Embeddings through Locality Encodings
Learning embeddings from large-scale networks is an open challenge.
Societal Controversies in Wikipedia Articles
Collaborative content creation inevitably reaches situations where different points of view lead to conflict.
Sharing emotions at scale: The Vent dataset
We present the Vent dataset, the largest annotated dataset of text, emotions, and social connections to date.
Social and Information Networks Human-Computer Interaction
