In this paper, we investigate a realistic but underexplored problem, called few-shot temporal
knowledge graph reasoning, that aims to predict future facts for newly emerging entities …

This paper investigates the few-shot temporal reasoning capability within the hyperbolic
space. The goal is to forecast future events for newly emerging entities within temporal …

This paper describes a novel diffusion model, DyDiff-VAE, for information diffusion prediction
on social media. Given the initial content and a sequence of forwarding users, DyDiff-VAE …

With the increasing demands on e-commerce platforms, numerous user action history is
emerging. Those enriched action records are vital to understand users' interests and intents …

This paper studies speculative reasoning task on real-world knowledge graphs (KG) that
contain both\textit {false negative issue}(ie, potential true facts being excluded) and\textit …

Temporal graphs, depicting time-evolving node connections through temporal edges, are
extensively utilized in domains where temporal connection patterns are essential, such as …

This paper addresses influence pathway discovery, a key emerging problem in today's
online media. We propose a discovery algorithm that leverages recently published work on …

This paper investigates cross-lingual temporal knowledge graph reasoning problem, which
aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource …

This paper introduces a fine-grained contrastive learning scheme for unsupervised node
clustering. Previous clustering methods only focus on a small feature set (class-dependent …

The proliferation of political memes in modern information campaigns calls for efficient
solutions for image classification by ideological affiliation. While significant advances have …