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Found 10 papers, 6 papers with code
Fast and Continual Knowledge Graph Embedding via Incremental LoRA
To address this issue, we propose a fast CKGE framework (\model), incorporating an incremental low-rank adapter (\mec) mechanism to efficiently acquire new knowledge while preserving old knowledge.
ALiiCE: Evaluating Positional Fine-grained Citation Generation
Large Language Models (LLMs) can enhance the credibility and verifiability by generating text with citations.
Towards Continual Knowledge Graph Embedding via Incremental Distillation
On the one hand, existing methods usually learn new triples in a random order, destroying the inner structure of new KGs.
Few-Shot Stance Detection via Target-Aware Prompt Distillation
The main challenge of this task comes two-fold: few-shot learning resulting from the varying targets and the lack of contextual information of the targets.
Modelling Universal Order Book Dynamics in Bitcoin Market
Understanding the emergence of universal features such as the stylized facts in markets is a long-standing challenge that has drawn much attention from economists and physicists.
Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification
Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node.
A Non-negative Symmetric Encoder-Decoder Approach for Community Detection
Latent factor models for community detection aim to find a distributed and generally low-dimensional representation, or coding, that captures the structural regularity of network and reflects the community membership of nodes.
Popularity Prediction on Social Platforms with Coupled Graph Neural Networks
In this paper, we consider the problem of network-aware popularity prediction, leveraging both early adopters and social networks for popularity prediction.
Cascade Dynamics Modeling with Attention-based Recurrent Neural Network
However, for cascade prediction, each cascade generally corresponds to a diffusion tree, causing cross-dependence in cascade— one sharing behavior could be triggered by its non-immediate predecessor in the memory chain.
