Social and Information Networks
New submissions
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New submissions for Mon, 20 May 24
- [1] arXiv:2405.10547 [pdf, other]
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Title: GPTs Window Shopping: An analysis of the Landscape of Custom ChatGPT ModelsComments: 9 pagesSubjects: Social and Information Networks (cs.SI)
OpenAI's ChatGPT initiated a wave of technical iterations in the space of Large Language Models (LLMs) by demonstrating the capability and disruptive power of LLMs. OpenAI has prompted large organizations to respond with their own advancements and models to push the LLM performance envelope. OpenAI has prompted large organizations to respond with their own advancements and models to push the LLM performance envelope. OpenAI's success in spotlighting AI can be partially attributed to decreased barriers to entry, enabling any individual with an internet-enabled device to interact with LLMs. What was previously relegated to a few researchers and developers with necessary computing resources is now available to all. A desire to customize LLMs to better accommodate individual needs prompted OpenAI's creation of the GPT Store, a central platform where users can create and share custom GPT models. Customization comes in the form of prompt-tuning, analysis of reference resources, browsing, and external API interactions, alongside a promise of revenue sharing for created custom GPTs. In this work, we peer into the window of the GPT Store and measure its impact. Our analysis constitutes a large-scale overview of the store exploring community perception, GPT details, and the GPT authors, in addition to a deep-dive into a 3rd party storefront indexing user-submitted GPTs, exploring if creators seek to monetize their creations in the absence of OpenAI's revenue sharing.
- [2] arXiv:2405.10558 [pdf, other]
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Title: CACL: Community-Aware Heterogeneous Graph Contrastive Learning for Social Media Bot DetectionComments: Accepted by ACL 2024 findingsSubjects: Social and Information Networks (cs.SI)
Social media bot detection is increasingly crucial with the rise of social media platforms. Existing methods predominantly construct social networks as graph and utilize graph neural networks (GNNs) for bot detection. However, most of these methods focus on how to improve the performance of GNNs while neglecting the community structure within social networks. Moreover, GNNs based methods still face problems such as poor model generalization due to the relatively small scale of the dataset and over-smoothness caused by information propagation mechanism. To address these problems, we propose a Community-Aware Heterogeneous Graph Contrastive Learning framework (CACL), which constructs social network as heterogeneous graph with multiple node types and edge types, and then utilizes community-aware module to dynamically mine both hard positive samples and hard negative samples for supervised graph contrastive learning with adaptive graph enhancement algorithms. Extensive experiments demonstrate that our framework addresses the previously mentioned challenges and outperforms competitive baselines on three social media bot benchmarks.
- [3] arXiv:2405.10640 [pdf, other]
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Title: COMET: NFT Price Prediction with Wallet ProfilingAuthors: Tianfu Wang, Liwei Deng, Chao Wang, Jianxun Lian, Yue Yan, Nicholas Jing Yuan, Qi Zhang, Hui XiongComments: Accepted by KDD 2024 (ADS Track)Subjects: Social and Information Networks (cs.SI)
As the non-fungible token (NFT) market flourishes, price prediction emerges as a pivotal direction for investors gaining valuable insight to maximize returns. However, existing works suffer from a lack of practical definitions and standardized evaluations, limiting their practical application. Moreover, the influence of users' multi-behaviour transactions that are publicly accessible on NFT price is still not explored and exhibits challenges. In this paper, we address these gaps by presenting a practical and hierarchical problem definition. This approach unifies both collection-level and token-level task and evaluation methods, which cater to varied practical requirements of investors. To further understand the impact of user behaviours on the variation of NFT price, we propose a general wallet profiling framework and develop a COmmunity enhanced Multi-bEhavior Transaction graph model, named COMET. COMET profiles wallets with a comprehensive view and considers the impact of diverse relations and interactions within the NFT ecosystem on NFT price variations, thereby improving prediction performance. Extensive experiments conducted in our deployed system demonstrate the superiority of COMET, underscoring its potential in the insight toolkit for NFT investors.
- [4] arXiv:2405.10818 [pdf, ps, other]
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Title: Modeling Supply Chain Interaction and Disruption: Insights from Real-world Data and Complex Adaptive SystemComments: arXiv admin note: text overlap with arXiv:2304.10428 by other authorsSubjects: Social and Information Networks (cs.SI)
In the rapidly evolving automotive industry, Systems-on-Chips (SoCs) are playing an increasingly crucial role in enhancing vehicle intelligence, connectivity, and safety features. For enterprises whose business encompasses automotive SoCs, the sustained and stable provision and receipt of SoC relevant goods or services are essential. Considering the imperative for a resilient and adaptable supply network, enterprises are concentrating their efforts on formulating strategies to address risks stemming from supply chain disruptions caused by technological obsolescence, natural disasters, and geopolitical tensions. This study presents an open supply knowledge extraction and complement approach and build a supply chain network of automotive SoC enterprises in China, which incorporates cross-domain named entity recognition under limited information, fuzzy matching of firm entities, and supply relation inferring based on knowledge graph. Subsequently, we exhibit the degree and registered capital distribution across firms, and analyze the correlations between centrality metrics in the supply chain network. Finally, based on recovery capacity and risk transfer, two interaction disruption models (IDMs) are developed to elucidate the adaptive behaviors and effect of network disruptions under various business and attack strategies. This research not only aids in exploring the complexities of Chinese automotive SoC supply chain but also enriches our understanding of the dynamics of firm behavior in this crucial industry sector.
Cross-lists for Mon, 20 May 24
- [5] arXiv:2405.10497 (cross-list from cs.MM) [pdf, other]
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Title: SMP Challenge: An Overview and Analysis of Social Media Prediction ChallengeAuthors: Bo Wu, Peiye Liu, Wen-Huang Cheng, Bei Liu, Zhaoyang Zeng, Jia Wang, Qiushi Huang, Jiebo LuoComments: ACM Multimedia. arXiv admin note: text overlap with arXiv:1910.01795Subjects: Multimedia (cs.MM); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Social and Information Networks (cs.SI)
Social Media Popularity Prediction (SMPP) is a crucial task that involves automatically predicting future popularity values of online posts, leveraging vast amounts of multimodal data available on social media platforms. Studying and investigating social media popularity becomes central to various online applications and requires novel methods of comprehensive analysis, multimodal comprehension, and accurate prediction.
SMP Challenge is an annual research activity that has spurred academic exploration in this area. This paper summarizes the challenging task, data, and research progress. As a critical resource for evaluating and benchmarking predictive models, we have released a large-scale SMPD benchmark encompassing approximately half a million posts authored by around 70K users. The research progress analysis provides an overall analysis of the solutions and trends in recent years. The SMP Challenge website (www.smp-challenge.com) provides the latest information and news.
Replacements for Mon, 20 May 24
- [6] arXiv:2304.12751 (replaced) [pdf, other]
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Title: Node Feature Augmentation Vitaminizes Network AlignmentComments: 18 pages, 12 figures, 5 tables; its conference version was presented at the ACM International Conference on Information and Knowledge Management (CIKM 2022)Subjects: Social and Information Networks (cs.SI); Information Theory (cs.IT); Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Networking and Internet Architecture (cs.NI)
- [7] arXiv:2310.10155 (replaced) [pdf, other]
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Title: Analysis and implementation of nanotargeting on LinkedIn based on publicly available non-PIIComments: 19 pages, 10 figuresJournal-ref: Proceedings of the CHI Conference on Human Factors in Computing Systems May 2024 Article No 1019 Pages 1 to 22Subjects: Social and Information Networks (cs.SI)
- [8] arXiv:2404.01319 (replaced) [pdf, other]
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Title: Information Cascade Prediction under Public Emergencies: A SurveyComments: arXiv admin note: text overlap with arXiv:2007.09815 by other authorsSubjects: Social and Information Networks (cs.SI); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
- [9] arXiv:1609.00004 (replaced) [pdf, other]
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Title: On the initial value of PageRankAuthors: Krishanu DeyasiComments: 11 pages, 15 figuresSubjects: Physics and Society (physics.soc-ph); Social and Information Networks (cs.SI)
- [10] arXiv:2311.03682 (replaced) [pdf, ps, other]
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Title: Incentive Design for Eco-driving in Urban Transportation NetworksSubjects: Systems and Control (eess.SY); Social and Information Networks (cs.SI); Optimization and Control (math.OC)
- [11] arXiv:2404.10228 (replaced) [pdf, other]
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Title: Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural NetworksSubjects: Machine Learning (cs.LG); Computation and Language (cs.CL); Social and Information Networks (cs.SI)
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