Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining最新文献_第4页

HGATs: hierarchical graph attention networks for multiple comments integration HGATs:用于多评论集成的分层图注意网络

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488322

Huixin Zhan, Kun Zhang, Chenyi Hu, Victor S. Sheng

{"title":"HGATs: hierarchical graph attention networks for multiple comments integration","authors":"Huixin Zhan, Kun Zhang, Chenyi Hu, Victor S. Sheng","doi":"10.1145/3487351.3488322","DOIUrl":"https://doi.org/10.1145/3487351.3488322","url":null,"abstract":"For decades, research in natural language processing (NLP) has focused on summarization. Sequence-to-sequence models for abstractive summarization have been studied extensively, yet generated summaries commonly suffer from fabricated content, and are often found to be near-extractive. We argue that, to address these issues, summarizers need to acquire the co-references that form multiple types of relations over input sentences, e.g., 1-to-N, N-to-1, and N-to-N relations, since the structured knowledge for text usually appears on these relations. By allowing the decoder to pay different attention to the input sentences for the same entity at different generation states, the structured graph representations generate more informative summaries. In this paper, we propose a hierarchical graph attention networks (HGATs) for abstractive summarization with a topic-sensitive PageRank augmented graph. Specifically, we utilize dual decoders, a sequential sentence decoder, and a graph-structured decoder (which are built hierarchically) to maintain the global context and local characteristics of entities, complementing each other. We further design a greedy heuristic to extract salient users' comments while avoiding redundancy to drive a model to better capture entity interactions. Our experimental results show that our models produce significantly higher ROUGE scores than variants without graph-based attention on both SSECIF and CNN/Daily Mail (CNN/DM) datasets.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124442943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Community deception in weighted networks 加权网络中的社区欺骗

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488337

Valeria Fionda, G. Pirrò

引用次数: 2

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3492719

Johan Fernquist, Björn Pelzer, Lukas Lundmark, Lisa Kaati, F. Johansson

引用次数: 0

Which acts model happiness?: an exploratory analysis on Twitter and Goodreads 哪些行为塑造了幸福?:对Twitter和Goodreads的探索性分析

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3489475

Mayank Bhasin, Harshit, Pawan Goyal

{"title":"Which acts model happiness?: an exploratory analysis on Twitter and Goodreads","authors":"Mayank Bhasin, Harshit, Pawan Goyal","doi":"10.1145/3487351.3489475","DOIUrl":"https://doi.org/10.1145/3487351.3489475","url":null,"abstract":"Modeling and analysis of affective and inner states is gaining prominence in research. Articulating the entire spectrum, ranging from recipes of long-term happiness to factors leading to depression, we frame a model of happiness states of people comprising of three states: G (lasting happiness), P (flickering) and I (frustration), respectively. The definitions of these states are based on psychology literature. We used a XgBoost Classifier to categorize 54,066 Twitter users based on their tweets and analysed the results including what kind of friends each category of users have (for 120 users obtained after thresholding 213 manually labelled users). Analysing XgBoost classification we could re-confirm characteristics mentioned in the definition of the three states (G, P, I) and find out more traits/characteristics beyond the definition as well. We observed that G users are more people-oriented. G and P users are more work-oriented than I users. G users are elder in age to P or I users. I users were found to be more religious than P owing to shelter-seeking traits. Qualitative analysis shows that G group suggests long-term vision, selfless and positive qualities, religious mindset and positive demeanour as expected. I group suggests negative feelings and activities and sensual words as expected. P group has traces of both G and I. P group contains dominating, strong words and extreme negative reactions. We found 21,115 users having Twitter and Goodreads handles to study what kind of books users of each category read. Reading patterns of G constitute of academic/technical, religion, inspirational/self-help and romance. Those of P users are fantasy/fiction, sports, LGBT/BDSM/Erotica and horror/violence/betrayal. I users tend to read fantasy/fiction, death and indiscriminately any arbitrary topic. G and P users make friends in the same category whereas I users tend to have friends in P category, but not among themselves.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130759111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictions of drug metabolism pathways through CYP 3A4 enzyme by analysing drug-target interactions network graph 通过分析药物-靶标相互作用网络图预测cyp3a4酶的药物代谢途径

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3490959

M. T. Albrijawi, Amrou Haj Ibrahim, R. Alhajj

引用次数: 0

Approximating 4-cliques in streaming graphs: the power of dual sampling 流图中近似4-团:双采样的威力

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3489471

Anmol Mann, Venkatesh Srinivasan, Alex Thomo

引用次数: 0

Automation of active reconnaissance phase: an automated API-based port and vulnerability scanner 主动侦察阶段的自动化:基于api的自动化端口和漏洞扫描器

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3492720

Malek Malkawi, Tansel Özyer, R. Alhajj

{"title":"Automation of active reconnaissance phase: an automated API-based port and vulnerability scanner","authors":"Malek Malkawi, Tansel Özyer, R. Alhajj","doi":"10.1145/3487351.3492720","DOIUrl":"https://doi.org/10.1145/3487351.3492720","url":null,"abstract":"The unprecedented growth in technology has increased the importance of the required information security that is still hard to be reached. Recently, network and web application attacks have occurred frequently, causing confidential data to be stolen by the available vulnerabilities in the systems and the most prominent is in the form of open ports. This causes the CIA (Confidentiality Integrity and Availability) Triad Model to break. Penetration testing is one of the key techniques used in real life to accurately detect the possible threats and potential attacks against the system, and the first step for hackers to conduct attacks is information collection. In this paper, we present a useful schema for the active information-gathering phase that can be used during penetration testing and by system administrators. It will be the first feature of a security engine going to be implemented. The work involves an automated API-based IP and port scanner, service-version enumerator, and vulnerability detection system. This scheme is based on the Network Mapper (Nmap) to collect the information with high accuracy depending on the provided rules in our schema. Besides, the work has been implemented as a RESTful-API server, aiming at easy integration for real-life cases and allowing administrators to scan and secure their networks more quickly and easily. The effectiveness and efficiency of this technique has been proved by the various test cases applied considering different scenarios from the real world. The average time of scanning a server and detecting the vulnerabilities is 2.2 minutes. Regardless of the number of vulnerabilities, the increase in time for each open port is just about 12 seconds.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123950378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The centrality of edges based on their role in induced triads 边的中心性基于它们在诱导三角中的作用

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3493825

Lauren Hudson, R. Whitaker, S. M. Allen, Liam D. Turner, Diane H Felmlee

{"title":"The centrality of edges based on their role in induced triads","authors":"Lauren Hudson, R. Whitaker, S. M. Allen, Liam D. Turner, Diane H Felmlee","doi":"10.1145/3487351.3493825","DOIUrl":"https://doi.org/10.1145/3487351.3493825","url":null,"abstract":"The prevalence of induced triads play an important role in characterising complex networks, supporting approaches for assessment of dynamic and partially obfuscated scenarios. In this paper we introduce a new local edge-centrality measure that is designed to be deployed in this context for complex networks and is highly scalable. It signifies the importance an edge plays within induced triads for a directed network. We observe that an edge can play one of two roles in providing connectivity within any particular triad, based on whether the edge supports connectivity to the third node or not. We call these alternative states overt and covert. As an edge may play alternative roles in different induced triads, this allows us to assess the local importance of an edge across multiple induced substructures. We introduce theory to count the number of induced triads in which an edge is overt and covert. Using 34 data sets derived from public sources, we show how the presence of overt and covert edges can be used to profile diverse real-world networks. The relationship with global network analysis metrics is examined. We observe that overt and covert edge centrality is useful in further differentiating classes of network, when considered in combination with conventional global network analysis metrics.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130227660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Will you dance to the challenge?: predicting user participation of TikTok challenges 你愿意跳舞迎接挑战吗?:预测TikTok挑战的用户参与度

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3488276

L. Ng, John Yeh Han Tan, Darryl Jing Heng Tan, R. Lee

{"title":"Will you dance to the challenge?: predicting user participation of TikTok challenges","authors":"L. Ng, John Yeh Han Tan, Darryl Jing Heng Tan, R. Lee","doi":"10.1145/3487351.3488276","DOIUrl":"https://doi.org/10.1145/3487351.3488276","url":null,"abstract":"TikTok is a popular new social media, where users express themselves through short video clips. A common form of interaction on the platform is participating in \"challenges\", which are songs and dances for users to iterate upon. Challenge contagion can be measured through replication reach, i.e., users uploading videos of their participation in the challenges. The uniqueness of the TikTok platform where both challenge content and user preferences are evolving requires the combination of challenge and user representation. This paper investigates social contagion of TikTok challenges through predicting a user's participation. We propose a novel deep learning model, deepChallenger, to learn and combine latent user and challenge representations from past videos to perform this user-challenge prediction task. We collect a dataset of over 7,000 videos from 12 trending challenges on the ForYouPage, the app's landing page, and over 10,000 videos from 1303 users. Extensive experiments are conducted and the results show that our proposed deepChallenger (F1=0.494) outperforms baselines (F1=0.188) in the prediction task.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127882540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Constraint-embedded paraphrase generation for commercial tweets 嵌入约束的商业推文释义生成

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI: 10.1145/3487351.3490974

Renhao Cui, G. Agrawal, R. Ramnath

引用次数: 1