85 research outputs found

    FairGen: Towards Fair Graph Generation

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    There have been tremendous efforts over the past decades dedicated to the generation of realistic graphs in a variety of domains, ranging from social networks to computer networks, from gene regulatory networks to online transaction networks. Despite the remarkable success, the vast majority of these works are unsupervised in nature and are typically trained to minimize the expected graph reconstruction loss, which would result in the representation disparity issue in the generated graphs, i.e., the protected groups (often minorities) contribute less to the objective and thus suffer from systematically higher errors. In this paper, we aim to tailor graph generation to downstream mining tasks by leveraging label information and user-preferred parity constraint. In particular, we start from the investigation of representation disparity in the context of graph generative models. To mitigate the disparity, we propose a fairness-aware graph generative model named FairGen. Our model jointly trains a label-informed graph generation module and a fair representation learning module by progressively learning the behaviors of the protected and unprotected groups, from the `easy' concepts to the `hard' ones. In addition, we propose a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability. Experimental results on seven real-world data sets, including web-based graphs, demonstrate that FairGen (1) obtains performance on par with state-of-the-art graph generative models across six network properties, (2) mitigates the representation disparity issues in the generated graphs, and (3) substantially boosts the model performance by up to 17% in downstream tasks via data augmentation

    An Inferable Representation Learning for Fraud Review Detection with Cold-start Problem

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    ยฉ 2019 IEEE. Fraud review significantly damages the business reputation and also customers' trust to certain products. It has become a serious problem existing on the current social media. Various efforts have been put in to tackle such problems. However, in the case of cold-start where a review is posted by a new user who just pops up on the social media, common fraud detection methods may fail because most of them are heavily depended on the information about the user's historical behavior and its social relation to other users, yet such information is lacking in the cold-start case. This paper presents a novel Joint-bEhavior-and-Social-relaTion-infERable (JESTER) embedding method to leverage the user reviewing behavior and social relations for cold-start fraud review detection. JESTER embeds the deep characteristics of existing user behavior and social relations of users and items in an inferable user-item-review-rating representation space where the representation of a new user can be efficiently inferred by a closed-form solution and reflects the user's most probable behavior and social relations. Thus, a cold-start fraud review can be effectively detected accordingly. Our experiments show JESTER (i) performs significantly better in detecting fraud reviews on four real-life social media data sets, and (ii) effectively infers new user representation in the cold-start problem, compared to three state-of-the-art and two baseline competitors

    Search Rank Fraud Prevention in Online Systems

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    The survival of products in online services such as Google Play, Yelp, Facebook and Amazon, is contingent on their search rank. This, along with the social impact of such services, has also turned them into a lucrative medium for fraudulently influencing public opinion. Motivated by the need to aggressively promote products, communities that specialize in social network fraud (e.g., fake opinions and reviews, likes, followers, app installs) have emerged, to create a black market for fraudulent search optimization. Fraudulent product developers exploit these communities to hire teams of workers willing and able to commit fraud collectively, emulating realistic, spontaneous activities from unrelated people. We call this behavior โ€œsearch rank fraudโ€. In this dissertation, we argue that fraud needs to be proactively discouraged and prevented, instead of only reactively detected and filtered. We introduce two novel approaches to discourage search rank fraud in online systems. First, we detect fraud in real-time, when it is posted, and impose resource consuming penalties on the devices that post activities. We introduce and leverage several novel concepts that include (i) stateless, verifiable computational puzzles that impose minimal performance overhead, but enable the efficient verification of their authenticity, (ii) a real-time, graph based solution to assign fraud scores to user activities, and (iii) mechanisms to dynamically adjust puzzle difficulty levels based on fraud scores and the computational capabilities of devices. In a second approach, we introduce the problem of fraud de-anonymization: reveal the crowdsourcing site accounts of the people who post large amounts of fraud, thus their bank accounts, and provide compelling evidence of fraud to the users of products that they promote. We investigate the ability of our solutions to ensure that fraud does not pay off

    Combating Threats to the Quality of Information in Social Systems

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    Many large-scale social systems such as Web-based social networks, online social media sites and Web-scale crowdsourcing systems have been growing rapidly, enabling millions of human participants to generate, share and consume content on a massive scale. This reliance on users can lead to many positive effects, including large-scale growth in the size and content in the community, bottom-up discovery of โ€œcitizen-expertsโ€, serendipitous discovery of new resources beyond the scope of the system designers, and new social-based information search and retrieval algorithms. But the relative openness and reliance on users coupled with the widespread interest and growth of these social systems carries risks and raises growing concerns over the quality of information in these systems. In this dissertation research, we focus on countering threats to the quality of information in self-managing social systems. Concretely, we identify three classes of threats to these systems: (i) content pollution by social spammers, (ii) coordinated campaigns for strategic manipulation, and (iii) threats to collective attention. To combat these threats, we propose three inter-related methods for detecting evidence of these threats, mitigating their impact, and improving the quality of information in social systems. We augment this three-fold defense with an exploration of their origins in โ€œcrowdturfingโ€ โ€“ a sinister counterpart to the enormous positive opportunities of crowdsourcing. In particular, this dissertation research makes four unique contributions: โ€ข The first contribution of this dissertation research is a framework for detecting and filtering social spammers and content polluters in social systems. To detect and filter individual social spammers and content polluters, we propose and evaluate a novel social honeypot-based approach. โ€ข Second, we present a set of methods and algorithms for detecting coordinated campaigns in large-scale social systems. We propose and evaluate a content- driven framework for effectively linking free text posts with common โ€œtalking pointsโ€ and extracting campaigns from large-scale social systems. โ€ข Third, we present a dual study of the robustness of social systems to collective attention threats through both a data-driven modeling approach and deploy- ment over a real system trace. We evaluate the effectiveness of countermeasures deployed based on the first moments of a bursting phenomenon in a real system. โ€ข Finally, we study the underlying ecosystem of crowdturfing for engaging in each of the three threat types. We present a framework for โ€œpulling back the curtainโ€ on crowdturfers to reveal their underlying ecosystem on both crowdsourcing sites and social media

    ์†Œ์…œ ๋„คํŠธ์›Œํฌ์™€ ์ด์ปค๋จธ์Šค ํ”Œ๋žซํผ์—์„œ์˜ ์ž ์žฌ ๋„คํŠธ์›Œํฌ ๋งˆ์ด๋‹

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2023. 2. ๊ถŒํƒœ๊ฒฝ.์›น ๊ธฐ๋ฐ˜ ์„œ๋น„์Šค์˜ ํญ๋ฐœ์ ์ธ ๋ฐœ๋‹ฌ๋กœ ์‚ฌ์šฉ์ž๋“ค์€ ์˜จ๋ผ์ธ ์ƒ์—์„œ ํญ๋„“๊ฒŒ ์—ฐ๊ฒฐ๋˜๊ณ  ์žˆ๋‹ค. ์˜จ๋ผ์ธ ํ”Œ๋žซํผ ์ƒ์—์„œ, ์‚ฌ์šฉ์ž๋“ค์€ ์„œ๋กœ์—๊ฒŒ ์˜ํ–ฅ์„ ์ฃผ๊ณ ๋ฐ›์œผ๋ฉฐ ์˜์‚ฌ ๊ฒฐ์ •์— ๊ทธ๋“ค์˜ ๊ฒฝํ—˜๊ณผ ์˜๊ฒฌ์„ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์ธ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ๋Œ€ํ‘œ์ ์ธ ์˜จ๋ผ์ธ ํ”Œ๋žซํผ์ธ ์†Œ์…œ ๋„คํŠธ์›Œํฌ ์„œ๋น„์Šค์™€ ์ด์ปค๋จธ์Šค ํ”Œ๋žซํผ์—์„œ์˜ ์‚ฌ์šฉ์ž ํ–‰๋™์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ์˜จ๋ผ์ธ ํ”Œ๋žซํผ์—์„œ์˜ ์‚ฌ์šฉ์ž ํ–‰๋™์€ ์‚ฌ์šฉ์ž์™€ ํ”Œ๋žซํผ ๊ตฌ์„ฑ ์š”์†Œ ๊ฐ„์˜ ๊ด€๊ณ„๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์‚ฌ์šฉ์ž์˜ ๊ตฌ๋งค๋Š” ์‚ฌ์šฉ์ž์™€ ์ƒํ’ˆ ๊ฐ„์˜ ๊ด€๊ณ„๋กœ, ์‚ฌ์šฉ์ž์˜ ์ฒดํฌ์ธ์€ ์‚ฌ์šฉ์ž์™€ ์žฅ์†Œ ๊ฐ„์˜ ๊ด€๊ณ„๋กœ ๋‚˜ํƒ€๋‚ด์ง„๋‹ค. ์—ฌ๊ธฐ์— ํ–‰๋™์˜ ์‹œ๊ฐ„๊ณผ ๋ ˆ์ดํŒ…, ํƒœ๊ทธ ๋“ฑ์˜ ์ •๋ณด๊ฐ€ ํฌํ•จ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‘ ํ”Œ๋žซํผ์—์„œ ์ •์˜๋œ ์‚ฌ์šฉ์ž์˜ ํ–‰๋™ ๊ทธ๋ž˜ํ”„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ž ์žฌ ๋„คํŠธ์›Œํฌ๋ฅผ ํŒŒ์•…ํ•˜๋Š” ์—ฐ๊ตฌ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์œ„์น˜ ๊ธฐ๋ฐ˜์˜ ์†Œ์…œ ๋„คํŠธ์›Œํฌ ์„œ๋น„์Šค์˜ ๊ฒฝ์šฐ ํŠน์ • ์žฅ์†Œ์— ๋ฐฉ๋ฌธํ•˜๋Š” ์ฒดํฌ์ธ ํ˜•์‹์œผ๋กœ ๋งŽ์€ ํฌ์ŠคํŠธ๊ฐ€ ๋งŒ๋“ค์–ด์ง€๋Š”๋ฐ, ์‚ฌ์šฉ์ž์˜ ์žฅ์†Œ ๋ฐฉ๋ฌธ์€ ์‚ฌ์šฉ์ž ๊ฐ„์— ์‚ฌ์ „์— ์กด์žฌํ•˜๋Š” ์นœ๊ตฌ ๊ด€๊ณ„์— ์˜ํ•ด ์˜ํ–ฅ์„ ํฌ๊ฒŒ ๋ฐ›๋Š”๋‹ค. ์‚ฌ์šฉ์ž ํ™œ๋™ ๋„คํŠธ์›Œํฌ์˜ ์ €๋ณ€์— ์ž ์žฌ๋œ ์‚ฌ์šฉ์ž ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์€ ํ™œ๋™ ์˜ˆ์ธก์— ๋„์›€์ด ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋น„์ง€๋„ํ•™์Šต ๊ธฐ๋ฐ˜์œผ๋กœ ํ™œ๋™ ๋„คํŠธ์›Œํฌ๋กœ๋ถ€ํ„ฐ ์‚ฌ์šฉ์ž ๊ฐ„ ์‚ฌํšŒ์  ๊ด€๊ณ„๋ฅผ ์ถ”์ถœํ•˜๋Š” ์—ฐ๊ตฌ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ๊ธฐ์กด์— ์—ฐ๊ตฌ๋˜์—ˆ๋˜ ๋ฐฉ๋ฒ•๋“ค์€ ๋‘ ์‚ฌ์šฉ์ž๊ฐ€ ๋™์‹œ์— ๋ฐฉ๋ฌธํ•˜๋Š” ํ–‰์œ„์ธ co-visitation์„ ์ค‘์ ์ ์œผ๋กœ ๊ณ ๋ คํ•˜์—ฌ ์‚ฌ์šฉ์ž ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์˜ˆ์ธกํ•˜๊ฑฐ๋‚˜, ๋„คํŠธ์›Œํฌ ์ž„๋ฒ ๋”ฉ ๋˜๋Š” ๊ทธ๋ž˜ํ”„ ์‹ ๊ฒฝ๋ง(GNN)์„ ์‚ฌ์šฉํ•˜์—ฌ ํ‘œํ˜„ ํ•™์Šต์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์€ ์ฃผ๊ธฐ์ ์ธ ๋ฐฉ๋ฌธ์ด๋‚˜ ์žฅ๊ฑฐ๋ฆฌ ์ด๋™ ๋“ฑ์œผ๋กœ ๋Œ€ํ‘œ๋˜๋Š” ์‚ฌ์šฉ์ž์˜ ํ–‰๋™ ํŒจํ„ด์„ ์ž˜ ํฌ์ฐฉํ•˜์ง€ ๋ชปํ•œ๋‹ค. ํ–‰๋™ ํŒจํ„ด์„ ๋” ์ž˜ ํ•™์Šตํ•˜๊ธฐ ์œ„ํ•ด, ANES๋Š” ์‚ฌ์šฉ์ž ์ปจํ…์ŠคํŠธ ๋‚ด์—์„œ ์‚ฌ์šฉ์ž์™€ ๊ด€์‹ฌ ์ง€์ (POI) ๊ฐ„์˜ ์ธก๋ฉด(Aspect) ์ง€ํ–ฅ ๊ด€๊ณ„๋ฅผ ํ•™์Šตํ•œ๋‹ค. ANES๋Š” User-POI ์ด๋ถ„ ๊ทธ๋ž˜ํ”„์˜ ๊ตฌ์กฐ์—์„œ ์‚ฌ์šฉ์ž์˜ ํ–‰๋™์„ ์—ฌ๋Ÿฌ ๊ฐœ์˜ ์ธก๋ฉด์œผ๋กœ ๋‚˜๋ˆ„๊ณ , ๊ฐ๊ฐ์˜ ๊ด€๊ณ„๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ํ–‰๋™ ํŒจํ„ด์„ ์ถ”์ถœํ•˜๋Š” ์ตœ์ดˆ์˜ ๋น„์ง€๋„ํ•™์Šต ๊ธฐ๋ฐ˜ ์ ‘๊ทผ ๋ฐฉ์‹์ด๋‹ค. ์‹ค์ œ LBSN ๋ฐ์ดํ„ฐ์—์„œ ์ˆ˜ํ–‰๋œ ๊ด‘๋ฒ”์œ„ํ•œ ์‹คํ—˜์—์„œ, ANES๋Š” ๊ธฐ์กด์— ์ œ์•ˆ๋˜์—ˆ๋˜ ๊ธฐ๋ฒ•๋“ค๋ณด๋‹ค ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ค€๋‹ค. ์œ„์น˜ ๊ธฐ๋ฐ˜ ์†Œ์…œ ๋„คํŠธ์›Œํฌ์™€๋Š” ๋‹ค๋ฅด๊ฒŒ, ์ด์ปค๋จธ์Šค์˜ ๋ฆฌ๋ทฐ ์‹œ์Šคํ…œ์—์„œ๋Š” ์‚ฌ์šฉ์ž๋“ค์ด ๋Šฅ๋™์ ์ธ ํŒ”๋กœ์šฐ/ํŒ”๋กœ์ž‰ ๋“ฑ์˜ ํ–‰์œ„๋ฅผ ์ˆ˜ํ–‰ํ•˜์ง€ ์•Š๊ณ ๋„ ํ”Œ๋žซํผ์— ์˜ํ•ด ์„œ๋กœ์˜ ์ •๋ณด๋ฅผ ์ฃผ๊ณ ๋ฐ›๊ณ  ์˜ํ–ฅ๋ ฅ์„ ํ–‰์‚ฌํ•˜๊ฒŒ ๋œ๋‹ค. ์ด์™€ ๊ฐ™์€ ์‚ฌ์šฉ์ž๋“ค์˜ ํ–‰๋™ ํŠน์„ฑ์€ ๋ฆฌ๋ทฐ ์ŠคํŒธ์— ์˜ํ•ด ์‰ฝ๊ฒŒ ์•…์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ฆฌ๋ทฐ ์ŠคํŒธ์€ ์‹ค์ œ ์‚ฌ์šฉ์ž์˜ ์˜๊ฒฌ์„ ์ˆจ๊ธฐ๊ณ  ํ‰์ ์„ ์กฐ์ž‘ํ•˜์—ฌ ์ž˜๋ชป๋œ ์ •๋ณด๋ฅผ ์ „๋‹ฌํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค. ๋‚˜๋Š” ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ์ž ๋ฆฌ๋ทฐ ๋ฐ์ดํ„ฐ์—์„œ ์‚ฌ์šฉ์ž ๊ฐ„ ์‚ฌ์ „ ๊ณต๋ชจ์„ฑ(Collusiveness)์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์ฐพ๊ณ , ์ด๋ฅผ ์ŠคํŒธ ํƒ์ง€์— ํ™œ์šฉํ•œ ๋ฐฉ๋ฒ•์ธ SC-Com์„ ์ œ์•ˆํ•œ๋‹ค. SC-Com์€ ํ–‰๋™์˜ ๊ณต๋ชจ์„ฑ์œผ๋กœ๋ถ€ํ„ฐ ์‚ฌ์šฉ์ž ๊ฐ„ ๊ณต๋ชจ ์ ์ˆ˜๋ฅผ ๊ณ„์‚ฐํ•˜๊ณ  ํ•ด๋‹น ์ ์ˆ˜๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ „์ฒด ์‚ฌ์šฉ์ž๋ฅผ ์œ ์‚ฌํ•œ ์‚ฌ์šฉ์ž๋“ค์˜ ์ปค๋ฎค๋‹ˆํ‹ฐ๋กœ ๋ถ„๋ฅ˜ํ•œ๋‹ค. ๊ทธ ํ›„ ์ŠคํŒธ ์œ ์ €์™€ ์ผ๋ฐ˜ ์œ ์ €๋ฅผ ๊ตฌ๋ณ„ํ•˜๋Š” ๋ฐ์— ์ค‘์š”ํ•œ ๊ทธ๋ž˜ํ”„ ๊ธฐ๋ฐ˜์˜ ํŠน์ง•์„ ์ถ”์ถœํ•˜์—ฌ ๊ฐ๋… ํ•™์Šต ๊ธฐ๋ฐ˜์˜ ๋ถ„๋ฅ˜๊ธฐ์˜ ์ž…๋ ฅ ๋ฐ์ดํ„ฐ๋กœ ํ™œ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. SC-Com์€ ๊ณต๋ชจ์„ฑ์„ ๊ฐ–๋Š” ์ŠคํŒธ ์œ ์ €์˜ ์ง‘ํ•ฉ์„ ํšจ๊ณผ์ ์œผ๋กœ ํƒ์ง€ํ•œ๋‹ค. ์‹ค์ œ ๋ฐ์ดํ„ฐ์…‹์„ ์ด์šฉํ•œ ์‹คํ—˜์—์„œ, SC-Com์€ ๊ธฐ์กด ๋…ผ๋ฌธ๋“ค ๋Œ€๋น„ ์ŠคํŒธ ํƒ์ง€์— ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์œ„ ๋…ผ๋ฌธ์—์„œ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด ์—ฐ๊ตฌ๋œ ์•”์‹œ์  ์—ฐ๊ฒฐ๋ง ํƒ์ง€ ๋ชจ๋ธ์€ ๋ ˆ์ด๋ธ”์ด ์—†๋Š” ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด์„œ๋„ ์‚ฌ์ „์— ์—ฐ๊ฒฐ๋˜์—ˆ์„ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์€ ์‚ฌ์šฉ์ž๋“ค์„ ์˜ˆ์ธกํ•˜๋ฏ€๋กœ, ์‹ค์‹œ๊ฐ„ ์œ„์น˜ ๋ฐ์ดํ„ฐ๋‚˜, ์•ฑ ์‚ฌ์šฉ ๋ฐ์ดํ„ฐ ๋“ฑ์˜ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ์—์„œ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์œ ์šฉํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜์—ฌ ๊ด‘๊ณ  ์ถ”์ฒœ ์‹œ์Šคํ…œ์ด๋‚˜, ์•…์„ฑ ์œ ์ € ํƒ์ง€ ๋“ฑ์˜ ๋ถ„์•ผ์—์„œ ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.Following the exploding usage on online services, people are connected with each other more broadly and widely. In online platforms, people influence each other, and have tendency to reflect their opinions in decision-making. Social Network Services (SNSs) and E-commerce are typical example of online platforms. User behaviors in online platforms can be defined as relation between user and platform components. A user's purchase is a relationship between a user and a product, and a user's check-in is a relationship between a user and a place. Here, information such as action time, rating, tag, etc. may be included. In many studies, platform user behavior is represented in graph form. At this time, the elements constituting the nodes of the graph are composed of objects such as users and products and places within the platform, and the interaction between the platform elements and the user can be expressed as two nodes being connected. In this study, I present studies to identify potential networks that affect the user's behavior graph defined on the two platforms. In ANES, I focus on representation learning for social link inference based on user trajectory data. While traditional methods predict relations between users by considering hand-crafted features, recent studies first perform representation learning using network/node embedding or graph neural networks (GNNs) for downstream tasks such as node classification and link prediction. However, those approaches fail to capture behavioral patterns of individuals ingrained in periodical visits or long-distance movements. To better learn behavioral patterns, this paper proposes a novel scheme called ANES (Aspect-oriented Network Embedding for Social link inference). ANES learns aspect-oriented relations between users and Point-of-Interests (POIs) within their contexts. ANES is the first approach that extracts the complex behavioral pattern of users from both trajectory data and the structure of User-POI bipartite graphs. Extensive experiments on several real-world datasets show that ANES outperforms state-of-the-art baselines. In contrast to active social networks, people are connected to other users regardless of their intentions in some platforms, such as online shopping websites and restaurant review sites. They do not have any information about each other in advance, and they only have a common point which is that they have visited or have planned to visit same place or purchase a product. Interestingly, users have tendency to be influenced by the review data on their purchase intentions. Unfortunately, this instinct is easily exploited by opinion spammers. In SC-Com, I focus on opinion spam detection in online shopping services. In many cases, my decision-making process is closely related to online reviews. However, there have been threats of opinion spams by hired reviewers increasingly, which aim to mislead potential customers by hiding genuine consumers opinions. Opinion spams should be filed up collectively to falsify true information. Fortunately, I propose the way to spot the possibility to detect them from their collusiveness. In this paper, I propose SC-Com, an optimized collusive community detection framework. It constructs the graph of reviewers from the collusiveness of behavior and divides a graph by communities based on their mutual suspiciousness. After that, I extract community-based and temporal abnormality features which are critical to discriminate spammers from other genuine users. I show that my method detects collusive opinion spam reviewers effectively and precisely from their collective behavioral patterns. In the real-world dataset, my approach showed prominent performance while only considering primary data such as time and ratings. These implicit network inference models studied on various data in this thesis predicts users who are likely to be pre-connected to unlabeled data, so it is expected to contribute to areas such as advertising recommendation systems and malicious user detection by providing useful information.Chapter 1 Introduction 1 Chapter 2 Social link Inference in Location-based check-in data 5 2.1 Background 5 2.2 Related Work 12 2.3 Location-based Social Network Service Data 15 2.4 Aspect-wise Graph Decomposition 18 2.5 Aspect-wise Graph learning 19 2.6 Inferring Social Relation from User Representation 21 2.7 Performance Analysis 23 2.8 Discussion and Implications 26 2.9 Summary 34 Chapter 3 Detecting collusiveness from reviews in Online platforms and its application 35 3.1 Background 35 3.2 Related Work 39 3.3 Online Review Data 43 3.4 Collusive Graph Projection 44 3.5 Reviewer Community Detection 47 3.6 Review Community feature extraction and spammer detection 51 3.7 Performance Analysis 53 3.8 Discussion and Implications 55 3.9 Summary 62 Chapter 4 Conclusion 63๋ฐ•

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