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Titlebook: Computer Supported Cooperative Work and Social Computing; 16th CCF Conference, Yuqing Sun,Tun Lu,Liping Gao Conference proceedings 2022 Spr

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樓主: Forbidding
11#
發(fā)表于 2025-3-23 13:18:59 | 只看該作者
12#
發(fā)表于 2025-3-23 14:50:49 | 只看該作者
Academic Article Classification Algorithm Based on?Pre-trained Model and?Keyword Extractionive source of academic information and play an important role in the process of delivering latest academic information. On social media, these academic articles will generate considerable academic news, translated articles, tutorial articles, etc. How to classify these academic articles has become m
13#
發(fā)表于 2025-3-23 20:09:21 | 只看該作者
14#
發(fā)表于 2025-3-24 02:02:33 | 只看該作者
Extractive-Abstractive: A Two-Stage Model for Long Text Summarizationxts with a clear structure, while abstractive method is suitable for short texts. In this paper, we aim to address the problems of missing key words and incomplete overview that are usually caused by abstractive method in the face of long texts. To solve this problem, we propose a two-stage model th
15#
發(fā)表于 2025-3-24 06:01:34 | 只看該作者
A Random-Walk-Based Heterogeneous Attention Network for?Community Detectionommunity have more dense connections than those in different communities, which can be utilized to analyze the function of complex networks. In addition, heterogeneous networks are ubiquitous in the real world. For example, academic networks have different types of nodes such as authors, papers, and
16#
發(fā)表于 2025-3-24 09:38:30 | 只看該作者
17#
發(fā)表于 2025-3-24 13:35:00 | 只看該作者
Adaptive Seed Expansion Based on?Composite Similarity for?Community Detection in?Attributed Networksle for real-world networks, it is useful to detect communities in attributed networks. Recently, many algorithms consider combinating node attributes and network topology, and the effect of these methods is better than using only one information source. However, the existing algorithms still have so
18#
發(fā)表于 2025-3-24 18:25:42 | 只看該作者
Conclusion: Creating a New Discourseons as feature information from high-dimensional data as well as multi-label data. Discriminative feature learning strengthens discrimination between sample features. Therefore, the feature information of samples can be better discriminated against in algorithms. In this paper, we propose a new unsu
19#
發(fā)表于 2025-3-24 20:53:18 | 只看該作者
20#
發(fā)表于 2025-3-25 03:03:12 | 只看該作者
,High Economic Growth, 1955–70, by investigating common university and college information service platforms, we find a problem that users cannot quickly access key information. Inspired by user profile and corporate portraits, we propose a university portrait system incorporating academic social networks. We first collect two ty
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