Information diffusion prediction, which aims to forecast future infected users during the information spreading process in social networks, is a challenging and critical task for applications such as social recommendation and fake news detection. Most existing methods primarily leverage GNNs and RNNs to capture structural and temporal patterns on social networks. However, the rich information contained in user profiles and post content is neglected, and the diffusion influence between users is challenging to model. To address these limitations, this paper proposes a fused attention-aware diffusion prediction method based on our fine-tuned Diffusion-LLM to improve the accuracy and interpretability of prediction results. The fused attention mechanism incorporates the diffusion influence analyzed by Diffusion-LLM and the user similarity matrix derived from a Transformer model based on user profiles. Preliminary experiments are conducted to verify the feasibility of our Diffusion-LLM.

Information diffusion prediction, which aims to forecast future infected users during the information spreading process on social platforms, is a challenging and critical task for public opinion analysis. With the development of social platforms, mass communication has become increasingly widespread. However, most existing methods based on GNNs and sequence models mainly focus on structural and temporal patterns in social networks, suffering from spurious diffusion connections and insufficient information for the diffusion analysis. We leverage strong reasoning capability of LLMs and develop a LL**M**-based causal framework for d**i**ffusion inf**l**uence **d**erivation (MILD). Comprehensively integrating four key factors of social diffusion, i.e., connections, active timelines, user profiles, and comments, MILD causally infers authentic diffusion links to construct a diffusion influence graph $G_I$. To validate the quality and reliability of our constructed graph $G_I$, we proposed a newly designed set of evaluation metrics w.r.t. diffusion prediction. We show MILD provides a reliable information diffusion structure that 12% absolutely better than the social network structure and achieves the state-of-the-art performance on diffusion prediction. MILD is expected to contribute to high-quality, more explainable, and more trustworthy public opinion analysis.

A graph is a fundamental data model to represent various entities and their complex relationships in society and nature, such as social networks, transportation networks, financial networks, and biomedical systems. Recently, large language models (LLMs) have showcased a strong generalization ability to handle various natural language processing tasks to answer users' arbitrary questions and generate specific-domain content. Compared with graph learning models, LLMs enjoy superior advantages in addressing the challenges of generalizing graph tasks by eliminating the need for training graph learning models and reducing the cost of manual annotation. However, LLMs are sequential models for textual data, but graphs are non-sequential topological data. It is challenging to adapt LLMs to tackle graph analytics tasks. In this survey, we conduct a comprehensive investigation of existing LLM studies on graph data, which summarizes the relevant graph analytics tasks solved by advanced LLM models and points out the existing challenges and future directions. Specifically, we study the key problems of LLM-based generative graph analytics (LLM-GGA) in terms of three categories: LLM-based graph query processing (LLM-GQP), LLM-based graph inference and learning (LLM-GIL), and graph-LLM-based applications. LLM-GQP focuses on an integration of graph analytics techniques and LLM prompts, includinggraph understandingandknowledge graphs and LLMs, while LLM-GIL focuses on learning and reasoning over graphs, includinggraph learning,graph-formed reasoning, andgraph representation. We summarize the useful prompts incorporated into LLM to handle different graph downstream tasks. Moreover, we give a summary of LLM model evaluation, benchmark datasets/tasks, and a deep pro and cons analysis of the discussed LLM-GGA models. We also explore open problems and future directions in this exciting interdisciplinary research area of LLMs and graph analytics.

Conversational humor is the key to capturing dialogue semantics and dialogue comprehension, which is usually generated in multiple modalities, such as linguistic rhetoric (textual modality), exaggerated facial expressions or movements (visual modality), and quirky intonation (acoustic modality). However, existing multimodal corpora for conversation humor are coarse-grained, and the modality is insufficient to support the conversational humor recognition task. This paper designed an annotation scheme for multimodal humor datasets, and constructed a corpus based on a Chinese sitcom for conversational humor recognition, named MUCH. The MUCH corpus consists of 34,804 utterances in total, and 7,079 of them are humorous. We employed both unimodal and multimodal methods to test our MUCH corpus. Experimental results showed that the multimodal approach could achieve 75.94% in terms of F1-score and surpassed the performance of most unimodal methods, which demonstrated that the MUCH corpus was effective for multimodal humor recognition tasks.