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Found 5 papers, 4 papers with code
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification
Existing self-supervised methods in natural language processing (NLP), especially hierarchical text classification (HTC), mainly focus on self-supervised contrastive learning, extremely relying on human-designed augmentation rules to generate contrastive samples, which can potentially corrupt or distort the original information.
A Simple yet Effective Method for Graph Classification
Inspired by structural entropy on graphs, we transform the data sample from graphs to coding trees, which is a simpler but essential structure for graph data.
Structural Optimization Makes Graph Classification Simpler and Better
We then present an implementation of the scheme in a tree kernel and a convolutional network to perform graph classification.
An Information-theoretic Perspective of Hierarchical Clustering
In this paper, we investigate hierarchical clustering from the \emph{information-theoretic} perspective and formulate a new objective function.
The idemetric property: when most distances are (almost) the same
We introduce the \emph{idemetric} property, which formalises the idea that most nodes in a graph have similar distances between them, and which turns out to be quite standard amongst small-world network models.
Social and Information Networks Discrete Mathematics
