Representation learning on heterogeneous spatiotemporal networks

“The problem of learning latent representations of heterogeneous networks with spatial and temporal attributes has been gaining traction in recent years, given its myriad of real-world applications. Most systems with applications in the field of transportation, urban economics, medical information, online e-commerce, etc., handle big data that can be structured into Spatiotemporal Heterogeneous Networks (SHNs), thereby making efficient analysis of these networks extremely vital. In recent years, representation learning models have proven to be quite efficient in capturing effective lower-dimensional representations of data. But, capturing efficient representations of SHNs continues to pose a challenge for the following reasons: (i) Spatiotemporal data that is structured as SHN encapsulate complex spatial and temporal relationships that exist among real-world objects, rendering traditional feature engineering approaches inefficient and compute-intensive; (ii) Due to the unique nature of the SHNs, existing representation learning techniques cannot be directly adopted to capture their representations. To address the problem of learning representations of SHNs, four novel frameworks that focus on their unique spatial and temporal characteristics are introduced: (i) collective representation learning, which focuses on quantifying the importance of each latent feature using Laplacian scores; (ii) modality aware representation learning, which learns from the complex user mobility pattern; (iii) distributed representation learning, which focuses on learning human mobility patterns by leveraging Natural Language Processing algorithms; and (iv) representation learning with node sense disambiguation, which learns contrastive senses of nodes in SHNs. The developed frameworks can help us capture higher-order spatial and temporal interactions of real-world SHNs. Through data-driven simulations, machine learning and deep learning models trained on the representations learned from the developed frameworks are proven to be much more efficient and effective”--Abstract, page iii

Creating Full Individual-level Location Timelines from Sparse Social Media Data

In many domain applications, a continuous timeline of human locations is critical; for example for understanding possible locations where a disease may spread, or the flow of traffic. While data sources such as GPS trackers or Call Data Records are temporally-rich, they are expensive, often not publicly available or garnered only in select locations, restricting their wide use. Conversely, geo-located social media data are publicly and freely available, but present challenges especially for full timeline inference due to their sparse nature. We propose a stochastic framework, Intermediate Location Computing (ILC) which uses prior knowledge about human mobility patterns to predict every missing location from an individual's social media timeline. We compare ILC with a state-of-the-art RNN baseline as well as methods that are optimized for next-location prediction only. For three major cities, ILC predicts the top 1 location for all missing locations in a timeline, at 1 and 2-hour resolution, with up to 77.2% accuracy (up to 6% better accuracy than all compared methods). Specifically, ILC also outperforms the RNN in settings of low data; both cases of very small number of users (under 50), as well as settings with more users, but with sparser timelines. In general, the RNN model needs a higher number of users to achieve the same performance as ILC. Overall, this work illustrates the tradeoff between prior knowledge of heuristics and more data, for an important societal problem of filling in entire timelines using freely available, but sparse social media data.Comment: 10 pages, 8 figures, 2 table

Context Aware POI Recommendation using Bipartite Graph

With the swift proliferation of handheld mobile devices, location based social networking (LBSNs) services have gained immense attention allowing users to discover their point of interest (POI). Application of collaborative filtering techniques in POI recommendation becomes challenging due to the sparsity of large user-POI rating matrix. Further, in the context of LBSNs, the spatiotemporal information is pivotal to capture user\u27s real-time preferences. In this work we propose a graph based POI recommendation approach, Context Aware POI with Social Trust (CAST) which integrates the geographical influence of the POIs and the influence of the social connections with the user rankings derived from the weighted bipartite graph. Experiments have been conducted with six state-of-the-art baselines using two real-world LBSN data sets. Findings reveal that user ranking on bipartite graph is a significant contributor to the performance along with social, geographical and spatial influence

소셜 네트워크와 이커머스 플랫폼에서의 잠재 네트워크 마이닝

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2023. 2. 권태경.웹 기반 서비스의 폭발적인 발달로 사용자들은 온라인 상에서 폭넓게 연결되고 있다. 온라인 플랫폼 상에서, 사용자들은 서로에게 영향을 주고받으며 의사 결정에 그들의 경험과 의견을 반영하는 경향을 보인다. 본 학위 논문에서는 대표적인 온라인 플랫폼인 소셜 네트워크 서비스와 이커머스 플랫폼에서의 사용자 행동에 대해 연구하였다. 온라인 플랫폼에서의 사용자 행동은 사용자와 플랫폼 구성 요소 간의 관계로 표현할 수 있다. 사용자의 구매는 사용자와 상품 간의 관계로, 사용자의 체크인은 사용자와 장소 간의 관계로 나타내진다. 여기에 행동의 시간과 레이팅, 태그 등의 정보가 포함될 수 있다. 본 연구에서는 두 플랫폼에서 정의된 사용자의 행동 그래프에 영향을 미치는 잠재 네트워크를 파악하는 연구를 제시한다. 위치 기반의 소셜 네트워크 서비스의 경우 특정 장소에 방문하는 체크인 형식으로 많은 포스트가 만들어지는데, 사용자의 장소 방문은 사용자 간에 사전에 존재하는 친구 관계에 의해 영향을 크게 받는다. 사용자 활동 네트워크의 저변에 잠재된 사용자 간의 관계를 파악하는 것은 활동 예측에 도움이 될 수 있으며, 이를 위해 본 논문에서는 비지도학습 기반으로 활동 네트워크로부터 사용자 간 사회적 관계를 추출하는 연구를 제안하였다. 기존에 연구되었던 방법들은 두 사용자가 동시에 방문하는 행위인 co-visitation을 중점적으로 고려하여 사용자 간의 관계를 예측하거나, 네트워크 임베딩 또는 그래프 신경망(GNN)을 사용하여 표현 학습을 수행하였다. 그러나 이러한 접근 방식은 주기적인 방문이나 장거리 이동 등으로 대표되는 사용자의 행동 패턴을 잘 포착하지 못한다. 행동 패턴을 더 잘 학습하기 위해, ANES는 사용자 컨텍스트 내에서 사용자와 관심 지점(POI) 간의 측면(Aspect) 지향 관계를 학습한다. ANES는 User-POI 이분 그래프의 구조에서 사용자의 행동을 여러 개의 측면으로 나누고, 각각의 관계를 고려하여 행동 패턴을 추출하는 최초의 비지도학습 기반 접근 방식이다. 실제 LBSN 데이터에서 수행된 광범위한 실험에서, ANES는 기존에 제안되었던 기법들보다 높은 성능을 보여준다. 위치 기반 소셜 네트워크와는 다르게, 이커머스의 리뷰 시스템에서는 사용자들이 능동적인 팔로우/팔로잉 등의 행위를 수행하지 않고도 플랫폼에 의해 서로의 정보를 주고받고 영향력을 행사하게 된다. 이와 같은 사용자들의 행동 특성은 리뷰 스팸에 의해 쉽게 악용될 수 있다. 리뷰 스팸은 실제 사용자의 의견을 숨기고 평점을 조작하여 잘못된 정보를 전달하는 방식으로 이루어진다. 나는 이를 해결하기 위해 사용자 리뷰 데이터에서 사용자 간 사전 공모성(Collusiveness)의 가능성을 찾고, 이를 스팸 탐지에 활용한 방법인 SC-Com을 제안한다. SC-Com은 행동의 공모성으로부터 사용자 간 공모 점수를 계산하고 해당 점수를 바탕으로 전체 사용자를 유사한 사용자들의 커뮤니티로 분류한다. 그 후 스팸 유저와 일반 유저를 구별하는 데에 중요한 그래프 기반의 특징을 추출하여 감독 학습 기반의 분류기의 입력 데이터로 활용하는 방법을 제시한다. SC-Com은 공모성을 갖는 스팸 유저의 집합을 효과적으로 탐지한다. 실제 데이터셋을 이용한 실험에서, SC-Com은 기존 논문들 대비 스팸 탐지에 뛰어난 성능을 보여주었다. 위 논문에서 다양한 데이터에 대해 연구된 암시적 연결망 탐지 모델은 레이블이 없는 데이터에 대해서도 사전에 연결되었을 가능성이 높은 사용자들을 예측하므로, 실시간 위치 데이터나, 앱 사용 데이터 등의 다양한 데이터에서 활용할 수 있는 유용한 정보를 제공하여 광고 추천 시스템이나, 악성 유저 탐지 등의 분야에서 기여할 수 있을 것으로 기대한다.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 63박

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

Link Prediction via Generalized Coupled Tensor Factorisation

This study deals with the missing link prediction problem: the problem of predicting the existence of missing connections between entities of interest. We address link prediction using coupled analysis of relational datasets represented as heterogeneous data, i.e., datasets in the form of matrices and higher-order tensors. We propose to use an approach based on probabilistic interpretation of tensor factorisation models, i.e., Generalised Coupled Tensor Factorisation, which can simultaneously fit a large class of tensor models to higher-order tensors/matrices with com- mon latent factors using different loss functions. Numerical experiments demonstrate that joint analysis of data from multiple sources via coupled factorisation improves the link prediction performance and the selection of right loss function and tensor model is crucial for accurately predicting missing links

A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector

The novel coronavirus disease (COVID-19) has rapidly spread around the globe in 2020, with the U.S. becoming the epicenter of COVID-19 cases since late March. As the U.S. begins to gradually resume economic activity, it is imperative for policymakers and power system operators to take a scientific approach to understanding and predicting the impact on the electricity sector. Here, we release a first-of-its-kind cross-domain open-access data hub, integrating data from across all existing U.S. wholesale electricity markets with COVID-19 case, weather, cellular location, and satellite imaging data. Leveraging cross-domain insights from public health and mobility data, we uncover a significant reduction in electricity consumption across that is strongly correlated with the rise in the number of COVID-19 cases, degree of social distancing, and level of commercial activity.Comment: This paper has been accepted for publication by Joule. The manuscript can also be accessed from EnerarXiv: http://www.enerarxiv.org/page/thesis.html?id=198