Titlebook: Computer Supported Cooperative Work and Social Computing; 18th CCF Conference, Yuqing Sun,Tun Lu,Bowen Du Conference proceedings 2024 The E

Flu表流動

確定方向

Incremental Inductive Dynamic Network Community Detectionel reuse for dynamic representation learning, constructs incremental node sets for updating the model, reduces training overhead, and quickly obtains node representation vectors for new moments of the network, then completing dynamic network community detection. IINDCD runs without reconstruction and with low retraining overhead.

某人

Er-EIR: A Chinese Question Matching Model Based on Word-Level and Sentence-Level Interaction Feature incorporate a co-attention mechanism to capture the interaction information between sentence pairs. By comparing our model with several baseline models on a self-built dataset of university financial question pairs, we have achieved remarkable performance.

collagen

MULTI

Prompt-Based and?Two-Stage Training for?Few-Shot Text Classificationecently, prompt-based learning has emerged as a powerful approach to handling a wide variety of tasks in NLP. It effectively bridges the gap between pre-trained language models (PLMs) and downstream tasks. Verbalizers are key components in prompt-based tuning. Existing manual prompts heavily rely on

偽書

A Fine-Grained Image Description Generation Method Based on?Joint Objectivest provide coherent and comprehensive textual details about the image content. Currently, most of these methods face two main challenges: description repetition and omission. Moreover, the existing evaluation metrics cannot clearly reflect the performance of models on these two issues. To address the

枕墊

Analyzing Collective Intelligence Through Sentiment Networks in?Self-organized Douban Communitiesn network communication research. In this study, we present an approach integrating the BERTopic topic model, advanced Natural Language Processing (NLP) techniques, and Social Network Analysis, to meticulously dissect the intricate dynamics of emotion propagation and evolution within collective beha

干涉

CULP

Scholar Influence Maximization via?Opinion Leader and?Graph Embedding Regression in?Social Networksence maximization problem. Most Existing influence maximization algorithms relying on scenario-specific centrality measures often underperform when applied to diverse social networks. Many influence maximization deep learning based algorithms use topological embedding to learn the global influence i