群體活動籌劃是個社群網路中重要且普遍的特色。然而，現有的群體活動
籌劃方法並不適用於涉及人與人之間的社群互動的活動。相關研究已表明參加
者之間的社群緊密度、參加者對活動的喜好，以及活動的多樣性等皆為群體
活動中的現場社群互動以及參加者滿意度的關鍵因素。相關研究亦顯示線下
群體活動中的現場社群互動易導致傳染病散播，其導致對參加者健康狀況的
負面影響。現有的線上群體活動籌劃方法並未詳加考慮上述關鍵因素。在本
論文中，我們藉由探討具有現場社群互動的線上群體活動的大眾化情境，克
服上述的缺點。更確切地來說，我們檢驗線上多串流直播群組籌劃的情境，
並提出一套綜合考慮了社群緊密度、參加者的喜好，以及多樣性等因素的查
詢，以順利籌劃線上多串流直播群組。我們制訂一套新的基礎查詢問題「社
群感知多樣化喜好群組查詢（Social-aware Diverse and Preferred Organization Query，SDSQ）」及其推廣版本GSPQ，其同時找出一組彼此間社群關係緊密的參與者，及一組受參與者喜愛的多樣化直播頻道，以籌劃線上多串流直播群組。我們證明上述查詢問題為NP－困難，並設計有效且快速的近似演算法SDSSel與GPDSel，以在保證求解品質的情況下近似於最佳解。我們也提出兩套剪枝策略加速SDSSel的運算。再者，我們探討針對線下群體活動籌劃的協作空間群眾外包的情境。確切來說，我們提出「傳染病感知最大化任務分配問
題（Epidemic-aware Maximum Task Assignment，EMTA）」，以組織協作工人群組，並將空間群眾外包任務分派給他們，同時考慮對傳染病散播的控制。我們證明EMTA為NP－困難，且在多項適時間內不可近似。我們又提出一套運用傳染病特性的「傳染病感知任務分配演算法（Epidemic-aware Task Assignment Algorithm，ETAA）」來完整解決EMTA問題。經由真實線上（適地性）社群網路與真實傳染病資料集上的大規模實驗，我們表明了我們提出的方法的有效性與高效率，超越了現有的比較基準方法。

Group activity organization is a critical and popular feature of social networks. Nevertheless, current approaches for group activity organization are not suitable for activities involving live social interaction between individuals. Research has shown the participants’ social closeness, their interest in the activities, and the diversity of activities are critical factors for live social interaction, and participant satisfaction during group activities. Research has also shown that live social interaction during offline group activities can lead to epidemic spread that negatively affects participant health outcomes.

Current online group activity organization approaches do not appropriately consider the above factors critical factors. In this dissertation, we address the aforementioned shortcomings by first studying a popular scenario of online group activities organization with live social interaction. More specifically, we examine the scenario of live multi-streaming soirees organization and propose a set of queries that concurrently consider social closeness, interest, and diversity factors to successfully organize an online multi-streaming soiree. We formulate a fundamental new query, Social-aware Diverse and Preferred Organization Query (SDSQ), and its extension, i.e., GSPQ, to jointly select a group of socially tight participants and a set of diverse and preferred live streams to organize a live multi-streaming soiree. We prove that the proposed queries are NP-hard and design efficient and effective algorithms, i.e., SDSSel and GPDSel, to approximate them with solution quality guarantee. We also propose two pruning strategies to boost SDSSel.

Furthermore, We study the scenario of collaborative spatial crowdsourcing for offline group activities organization. Specifically, we propose Epidemic-aware Maximum Task Assignment (EMTA) to form and assign collaborative worker groups to spatial crowdsourcing tasks while taking into consideration the control of epidemic spread. We prove that EMTA is NP-hard and inapproximable in polynomial time unless P=NP. We then propose the Epidemic-aware Task Assignment Algorithm (ETAA) that leverages epidemic characteristics to fully address EMTA.

Through extensive experiments on real social networks (OSNs and LBSNs)
datasets and real epidemic datasets, we demonstrate the efficiency and effectiveness of our proposed approaches and strategies, as well as the superiority of our proposed approaches over the baselines.

Abstract (Chinese) I
Abstract II
Acknowledgements IV
Contents VI
List of Figures X
List of Tables XII
List of Algorithms XIII
1 Introduction 1
1.0.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.0.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.0.3 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . 7
1.0.4 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.0.5 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2 Literature Review 13
2.1 Online Group Activity Organization Approaches . . . . . . . . . . . 13
2.1.1 Recommendation Approaches . . . . . . . . . . . . . . . . . 13
2.1.2 Group and Team Formation Approaches . . . . . . . . . . . 16
2.2 Offline Group Activity Organization Approaches . . . . . . . . . . . 17
2.2.1 POIs Recommendation . . . . . . . . . . . . . . . . . . . . . 18
2.2.2 Social, Spatial and Temporal Queries . . . . . . . . . . . . . 18
2.2.3 Spatial Crowdsourcing . . . . . . . . . . . . . . . . . . . . . 19
3 Live Multi-Streaming Soiree Organization 21
3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2.1 Critical Multi-Streaming Features . . . . . . . . . . . . . . . 23
3.3 Problem Presentation . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.3.1 Critical Factors for our Approach . . . . . . . . . . . . . . . 26
3.3.2 Query Presentation . . . . . . . . . . . . . . . . . . . . . . . 27
3.3.3 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . 28
3.3.4 Hardness Analysis . . . . . . . . . . . . . . . . . . . . . . . 31
3.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.4.1 Integer Linear Programming Formulation . . . . . . . . . . . 32
3.4.2 Algorithm Design for SDSQ . . . . . . . . . . . . . . . . . . 35
3.4.3 Pruning Strategies . . . . . . . . . . . . . . . . . . . . . . . 44
3.4.4 Generalized Social-aware Maximum Preferred and Diverse
Query . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.4.5 Improvements with Parametric Maximum Flows . . . . . . . 62
3.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.5.1 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . 66
3.5.2 Experimental Results of Algorithms for SDSQ . . . . . . . . 69
3.5.3 Experimental Results of Pruning Strategies . . . . . . . . . . 72
3.5.4 Experimental Results of Algorithms for GSPQ . . . . . . . . 74
3.6 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.6.1 Applicability . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.6.2 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . 77
3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4 Collaborative Spatial Crowdsourcing 82
4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.3 Problem Presentation . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.3.1 Critical Factors for our Approach . . . . . . . . . . . . . . . 85
4.3.2 Query Presentation . . . . . . . . . . . . . . . . . . . . . . . 86
4.4 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.4.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.4.2 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . 90
4.4.3 Derivation of Infection Stage Probabilities with Contact Social
Networks . . . . . . . . . . . . . . . . . . . . . . . . . . 94
4.4.4 Hardness Analysis of EMTA . . . . . . . . . . . . . . . . . . 97
4.5 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
4.5.1 Algorithm Design . . . . . . . . . . . . . . . . . . . . . . . . 98
4.6 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.6.1 Solution Quality . . . . . . . . . . . . . . . . . . . . . . . . 110
4.6.2 Epidemic Control . . . . . . . . . . . . . . . . . . . . . . . . 111
4.6.3 Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
5 Conclusion 114
Bibliography 116
A User Study 131
B Multi-Group Queries Extension 135
B.0.1 Extension of Max-Flow Min-Cut for Top k Sets . . . . . . . 137
B.0.2 Algorithm Design of kSDSSel-I . . . . . . . . . . . . . . . . 140
B.0.3 Algorithms Design of kGPDSel-I . . . . . . . . . . . . . . . 142
B.0.4 Theoretical Analysis of the Multi-Group Algorithms . . . . . 143

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