臺灣博碩士論文加值系統

隨著行動裝置和各種感知器的發展，許多事件可以被紀錄為時空間紀錄，例如犯罪資料、交通資料以及打卡資料等。時空間數據挖掘已經成為熱門的研究議題且吸引許多研究學者的關注。如何萃取和分析空間感知資料庫中的資訊來進行模式挖掘、朋友推薦或是景點推薦已經成為一個具有吸引力與挑戰型的議題。在本文中，我們旨在發展一系列新穎且有效用的數據挖掘框架，並且分別利用這些框架來進行時空間鏈結模式挖掘、主動式熟人推測以及屏蔽性打卡推薦。

在位置感知資料庫中採用不具身分事件特徵來挖掘時空間鏈結模式：
時空間模式挖掘嘗試在特定的時間區間以及空間區域中去尋找未知、有趣和有用的事件序列。在文獻中，時空見序列模式挖掘通常於存在身分資訊的資料中進行。對於近期火紅的開放資料，大部分的開放資料由於隱私顧慮的關係是沒有特定身分資訊的，先前的研究將會遇到將這種不具身分資料轉換進挖掘流程的難題。在本研究中，我們提出了一種實用的方法，稱為前K個時空間鏈結模式搜尋(簡稱為TKSTP)，用此方法來找出頻繁的時空間鏈結模式。我們運用TKSTP的框架在兩個沒有身分資訊的真實犯罪數據集中，我們的實驗結果顯示提出的框架有效地找出高質量的時空間鏈結模式。此外犯罪模式分析的案例也證明了這框架的實用性，並且揭露了幾個有趣的隱藏現象。

在位置感知資料庫中採用打卡事件特徵的主動式學習方法來進行熟人推測：
隨著移動設備和各種感測器的普及，相較於以前，現在可以很輕鬆地存取每個人的地理性活動。人與人之間的社交聯繫對某些應用有相當大的幫助，像是可以識別恐怖份子或者使推薦系統更加精準，然而，由於隱私的議題，很難去獲取到這些只有服務提供商所擁有的社交聯繫關係。在本論文中，我們提出了位置感知的相識者推論問題，該問題的目的是基於人們的本地地理活動資訊(例如Instagram中標記地理位置的帖子和Meetup中的舉辦於某個地理位置的會議事件)來找尋任何給定查詢人的相識者。
我們提出了利用主動學習的概念來解決LAI問題，我們開發了一種新穎的半監督模型，稱之為主動式增強隨機漫步(簡稱ARW)模型，此模型將主動是學習的概念加入至活動關係圖中的重啟式隨機漫步。具體而言，我們設計了一系列的候選人選擇策略來選擇哪個尚未被標記的人被標記後，對於模型可以有較大的貢獻度，並且當標記完成後，會對活動關係圖做出相對應的修正來指引重啟式隨機漫步的運作。進行於Instagram和Meetup這兩種數據集的實驗展現出我們提出的方法優於這些最先進的方法，必且透過一系列的實驗設置，ARW在不同的真實情境下都能得到令人滿意的結果。

在位置感知資料庫中採用打卡事件特徵來進行屏蔽性打卡以對抗熟人推測：
適地性社交服務（例如：Foursquare和Facebook Place）允許用戶可以在造訪地點進行打卡並且與他人在地理空間上可以互動(例如：一起打卡)，現有的研究表示，人們可以透過打卡的資料來準確地推斷其社交關係，使得傳統的打卡系統也無法保護使用者的相識者隱私。我們因此提出了一種新穎的打卡屏蔽系統，此系統的目標在於引導使用者可以打卡於安全的地點上。我們也因此提出了一個新的研究問題，即是利用打卡屏蔽來防止相識者推斷(簡稱為CSAI)，此研究問題的目的在於當使用者打算進行打卡時，推薦一個安全地點的列表，而在使用者打卡於安全地點後，能使得相識者推斷的準確率大幅下降。我們開發了打卡屏蔽計畫(簡稱CSS)系統來解決CSAI的問題，CSS包含了兩步驟，第一步是評估使用者間的社群強度，第二步則是產生一個安全地點列表。進行於Foursquare和Gowalla這兩種打卡數據集的實驗展現出CSS的屏蔽效果不僅在各種情境設定下都能超越其他方法，並且也能夠維持打卡距離和保證屏蔽推薦的打卡資料在地點推薦上的可用性。

With the advance in mobile devices and various sensors, many events can be noted as spatio-temporal records such as crime data, traffic data, and check-in data, and so on. Spatio-temporal mining has become the emerging research fields that attract a lot of attention. How to extract and analyze in location-aware databases for mining patterns and recommending friends or point-of-interest has become an attractive and challenging issue over the past few years.
In this dissertation, we develop a series of novel and effective data mining frameworks for mining spaito-temporal chaining patterns, actively inferring acquaintance and recommending shielding check-in.

Mining Spatio-Temporal Chaining Patterns with Non-identity Event in Location-aware Databases:
Spatio-temporal pattern mining attempts to discover unknown, potentially interesting and useful event sequences in which events occur within a specific time interval and spatial region. In the literature, mining of spatio-temporal sequential patterns generally relies on the existence of identity information for the accumulation of pattern appearances. For the recent trend of open data, which are mostly released without the specific identity information due to privacy concern, previous work will encounter the challenging difficulty to properly transform such extit{non-identity data into the mining process. In this work, we propose a practical approach, called emph{Top K Spatio-Temporal Chaining Patterns Discovery (abbreviated as emph{TKSTP), to discover frequent spatio-temporal chaining patterns. The emph{TKSTP framework is applied on two real criminal datasets which are released without the identity information. Our experimental studies show that the proposed framework effectively discovers high-quality spatio-temporal chaining patterns. In addition, case studies of crime pattern analysis also demonstrate their applicability and reveal several interestingly hidden phenomenons.

Active Learning-based Approach for Acquaintance Inference with Check-in Event Features in Location-aware Databases:
With the popularity of mobile devices and various sensors, the local geographical activities of human beings can be easily accessed than ever. Yet due to the privacy concern, it is difficult to acquire the social connections among people possessed by services providers, which can benefit applications such as identifying terrorists and recommender systems. In this work, we propose the extit{Location-aware Acquaintance Inference (LAI) problem, which aims at finding the acquaintances for any given query individual based on extit{solely people's extit{local geographical activities, such as geo-tagged posts in Instagram and meeting events in Meetup, within a targeted geo-spatial area. We propose to leverage the concept of extit{active learning to tackle the LAI problem. We develop a novel semi-supervised model, extit{Active Learning-enhanced Random Walk (ARW), which imposes the idea of active learning into the technique of Random Walk with Restart (RWR) in an extit{activity graph. Specifically, we devise a series of extit{Candidate Selection strategies to select unlabeled individuals for labeling, and perform the different extit{Graph Refinement mechanisms that reflect the labeling feedback to guide the RWR random surfer. Experiments conducted on Instagram and Meetup datasets exhibit the promising performance, compared with a set of state-of-the-art methods. With a series of empirical settings, ARW is demonstrated to derive satisfying results of acquaintance inference in different real scenarios.

Shielding Check-in Recommendation against Acquaintance Inference with Check-in Event Features in Location-aware Databases:
Location-based social services such as Foursquare and Facebook Place allow users to perform check-ins at places and interact with each other in geography (e.g. check-in together). While existing studies have exhibited that the adversary can accurately infer social ties based on check-in data, the traditional check-in mechanism cannot protect the acquaintance privacy of users. Therefore, we propose a novel extit{shielding check-in system, whose goal is to guide users to check-in at secure places. We accordingly propose a novel research problem, extit{Check-in Shielding against Acquaintance Inference (CSAI), which aims at recommending a list of secure places when users intend to check-ins so that the potential that the adversary correctly identifies the friends of users can be significantly reduced. We develop the extit{Check-in Shielding Scheme (CSS) framework to solve the CSAI problem. CSS consists of two steps, namely estimating the social strength between users and generating a list of secure places. Experiments conducted on Foursquare and Gowalla check-in datasets show that CSS is able to not only outperform several competing methods under various scenario settings, but also lead to the check-in distance preserving and ensure the usability of the new check-in data in Point-of-Interest (POI) recommendation.

中文摘要 ............................................ i
Abstract............................................. iii
Acknowledgment ........................................ vi
Contents............................................. vii
List of Tables.......................................... xi
List of Figures ......................................... xii
1 Introduction......................................... 1
1.1 Motivation....................................... 1
1.2 Overview of the Dissertation............................. 2
1.2.1 Mining Spatio-Temporal Chaining Patterns with Non-identity Event . . . 3
1.2.2 Active Learning-based Approach for Acquaintance Inference with Check-in Event.................................... 4
1.2.3 Shielding Check-in Recommendation against Acquaintance Inference with Check-in Event ................................ 5
1.3 Organization of the Dissertation........................... 6
2 Mining Spatio-Temporal Chaining Patterns with Non-identity Event . . . . . . . . . . 7
2.1 Introduction...................................... 7
2.2 Related Work of Spatio-Temporal Pattern Mining . . . . . . . . . . . . . . . . . 11
2.2.1 Spatio-Temporal Data Mining........................ 11
2.2.2 Sequential Pattern Mining.......................... 14
2.2.3 Spatio-Temporal Pattern Mining on Non-Identity Data . . . . . . . . . . 14
2.3 Preliminaries of Spatial-Temporal Chaining Pattern . . . . . . . . . . . . . . . . 15
2.3.1 Problem Formulation............................. 15
2.3.2 Containment Relation ............................ 17
2.3.3 Redundant Counting Phenomenon ..................... 18
2.3.4 Non-Downward-Closure Property ...................... 19
2.4 Mining Top K Spatio-Temporal Chaining Patterns. . . . . . . . . . . . . . . . . 20
2.4.1 Spatial Temporal Filtering.......................... 22
2.4.2 Temporal Spatial Filtering.......................... 24
2.4.3 Top K ST-patterns Discovery Using General Generation . . . . . . . . . 26
2.4.4 Top K ST-patterns Discovery Using Downward-Closure Generation . . . 27
2.5 Experimental Results ................................ 33
2.5.1 Data Description ............................... 33
2.5.2 The Results of Phily Crime Dataset ..................... 34
2.5.3 The Results of SpotCrime Dataset ..................... 37
2.5.4 Accuracy Performance Evaluation...................... 40
2.5.5 Execution Time Analysis........................... 44
2.6 Summary ....................................... 49
3 Active Learning-based Approach for Acquaintance Inference with Check-in Event . . 50
3.1 Introduction...................................... 50
3.2 Related Work of Acquaintance Inference ...................... 54
3.3 Problem Statement.................................. 57
3.4 Methodology ..................................... 59
3.4.1 Activity Graph Construction......................... 60
3.4.2 RWR-based Acquaintance Inference..................... 62
3.4.3 Active Learning-enhanced RandomWalk.................. 62
3.4.4 Time Complexity Analysis of ARW..................... 69
3.5 Evaluation....................................... 69
3.5.1 Datasets.................................... 70
3.5.2 Competitors and Evaluation Metric..................... 72
3.5.3 Experimental Results............................. 78
3.6 Summary ....................................... 86
4 Shielding Check-in Recommendation with Check-in Event. . . . . . . . . . . . . . . . 87
4.1 Introduction...................................... 87
4.2 Related Work of Privacy Preserving in Social Service . . . . . . . . . . . . . . . 92
4.3 Problem Formulation................................. 95
4.4 Methodology ..................................... 99
4.4.1 Social Strength Quantification........................100
4.4.2 Shielding Place List Generation .......................105
4.5 Evaluation.......................................109
4.5.1 Data Description ...............................110
4.5.2 Evaluation Settings..............................110
4.5.3 Experimental Results.............................112
4.6 Summary .......................................130
5 Conclusions .........................................131
Reference ............................................133

[1] M. Hayn, S. Beirle, F. A. Hamprecht, U. Platt, B. H. Menze, and T. Wagner, “Analysing spatio-temporal patterns of the global no 2-distribution retrieved from gome satellite observations using a generalized additive model, Atmospheric chemistry and physics, 2009.
[2] K. Koperski and J. Han, “Discovery of spatial association rules in geographic information databases, in Proceedings of the International Symposium on Advances in Spatial Databases, 1995.
[3] Y. Morimoto, “Mining frequent neighboring class sets in spatial databases, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001.
[4] G. Das, K. ip Lin, H. Mannila, G. Renganathan, and P. Smyth, “Rule discovery from time series, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1998.
[5] J. Yang, W. Wang, and S. Y. Philip, “Mining surprising periodic patterns, Data Mining and Knowledge Discovery, vol. 9, no. 2, 2004.
[6] H. Cao, N. Mamoulis, and D. W. Cheung, “Mining frequent spatio-temporal sequential patterns, in Proceedings of IEEE International Conference on Data Mining (ICDM), 2005.
[7] F. Verhein and S. Chawla, “Mining spatio-temporal association rules, sources, sinks, stationary regions and thoroughfares in object mobility databases, in Proceedings of Database Systems for Advanced Applications, 2006.
[8] K. Leong and A. Sung, “A review of spatio-temporal pattern analysis approaches on crime analysis, International E-journal of Criminal Sciences, vol. 9, 2015.
[9] J. Refonaa, M. Lakshmi, and V. Vivek, “Analysis and prediction of natural disaster using spatial data mining technique, in Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies (ICCPCT), 2015.
[10] K. V. Rao, A. Govardhan, and K. C. Rao, “Spatiotemporal data mining: Issues, tasks and applications, International Journal of Computer Science ＆ Engineering Survey, vol. 3, 2012.
[11] R. Medina and G. Hepner, Geospatial Analysis of Dynamic Terrorist Networks. Springer Netherlands, 2008.
[12] R. M. Medina and G. F. Hepner, “The geography of international terrorism: An introduction to spaces and places of violent non-state groups, CRC Press, 2013.
[13] M. K. Sparrow, “The application of network analysis to criminal intelligence: An assessment of the prospects, Social Networks, vol. 13, no. 3, 1991.
[14] B. Hu and M. Ester, “Social topic modeling for point-of-interest recommendation in location-based social networks, in Proceedings of IEEE International Conference on Data Mining (ICDM), 2014.
[15] H. Li, Y. Ge, R. Hong, and H. Zhu, “Point-of-interest recommendations: Learning potential check-ins from friends, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
[16] A. Sadilek, H. A. Kautz, and J. P. Bigham, “Finding your friends and following them to where you are, in Proceedings of International Conference on Web Search and Web Data Mining (WSDM), 2012.
[17] J.-D. Zhang and C.-Y. Chow, “Geosoca: Exploiting geographical, social and categorical correlations for point-of-interest recommendations, in Proceedings of ACM SIGIR International Conference on Research and Development in Information Retrieval, 2015.
[18] H. Wang, Z. Li, and W. Lee, “Pgt: Measuring mobility relationship using personal, global and temporal factors, in Proceedings of IEEE International Conference on Data Mining (ICDM), 2014.
[19] M. Fire, R. Goldschmidt, and Y. Elovici, “Online social networks: Threats and solutions, IEEE Communications Surveys and Tutorials, vol. 16, no. 4, 2014.
[20] R. Dey, Z. Jelveh, and K. W. Ross, “Facebook users have become much more private: A large-scale study, in Workshop Proceedings of IEEE International Conference on Pervasive Computing and Communications (PerCom), 2012.
[21] R. Cheng, J. Pang, and Y. Zhang, “Inferring friendship from check-in data of location-based social networks, in Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2015.
[22] J. Cranshaw, E. Toch, J. I. Hong, A. Kittur, and N. M. Sadeh, “Bridging the gap between physical location and online social networks, in Proccedings of International Conference on Ubiquitous Computing (UbiComp), 2010.
[23] H. Hsieh, R. Yan, and C. Li, “Where you go reveals who you know: Analyzing social ties from millions of footprints, in Proceedings of ACM CIKM International Conference on Information and Knowledge Management, 2015.
[24] G. S. Njoo, M. Kao, K. Hsu, and W. Peng, “Exploring check-in data to infer social ties in location based social networks, in Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2017.
[25] H. Pham, C. Shahabi, and Y. Liu, “EBM: an entropy-based model to infer social strength from spatiotemporal data, in Proceedings of the ACM SIGMOD International Conference on Management of Data, 2013.
[26] P.-N. Tan, M. Steinbach, and V. Kumar, “Chapter 6. association analysis: Basic concepts and algorithms, Introduction to Data Mining, 2005.
[27] Y. Huang, L. Zhang, and P. Zhang, “A framework for mining sequential patterns from spatio-temporal event data sets, IEEE Transactions on Knowledge and Data Engineering, 2008.
[28] R. Agrawal and R. Srikant, “Mining sequential patterns, in Proceedings of IEEE ICDE International Conference on Data Engineering, 1995.
[29] L. Chen and J. Jakubowicz, “Inferring bike trip patterns from bike sharing system open data, in Proceedings of IEEE BigData International Conference on Big Data, 2015.
[30] S. S. Rahman, J. M. Easton, and C. Roberts, “Mining open and crowdsourced data to improve situational awareness for railway, in Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2015.
[31] T. Almanie, R. Mirza, and E. Lor, “Crime prediction based on crime types and using spatial and temporal criminal hotspots, International Journal of Data Mining ＆ Knowledge Management Process, vol. 5, 2015.
[32] K. Leong, J. Li, S. C.-F. Chan, and V. T. Ng, “An application of the dynamic pattern analysis framework to the analysis of spatial-temporal crime relationships. Journal of Universal Computer Science, 2009.
[33] E. Cho, S. A. Myers, and J. Leskovec, “Friendship and mobility: user movement in location-based social networks, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011.
[34] A. Guttman, “R-trees: A dynamic index structure for spatial searching, in Proceedings of ACM SIGMOD International Conference on Management of Data, 1984.
[35] N. Askitis, “Fast and compact hash tables for integer keys, in Proceedings of Australasian Computer Science Conference (ACSC), 2009.
[36] H. Cao, N. Mamoulis, and D. W. Cheung, “Discovery of periodic patterns in spatiotemporal sequences, IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 4, 2007.
[37] N. Mamoulis, H. Cao, G. Kollios, M. Hadjieleftheriou, Y. Tao, and D. W. Cheung, “Mining, indexing, and querying historical spatiotemporal data, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004.
[38] Z. Li, B. Ding, J. Han, R. Kays, and P. Nye, “Mining periodic behaviors for moving objects, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010.
[39] Z. Li, J. Wang, and J. Han, “Mining periodicity for sparse and incomplete event data, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012.
[40] J. Han, G. Dong, and Y. Yin, “E cient mining of partial periodic patterns in time series database, in Proceedings of IEEE ICDE International Conference on Data Engineering, 1999.
[41] H. Jeung, Q. Liu, H. T. Shen, and X. Zhou, “A hybrid prediction model for moving objects, in Proceedings of IEEE ICDE International Conference on Data Engineering, 2008.
[42] Y. Tao, C. Faloutsos, D. Papadias, and B. Liu, “Prediction and indexing of moving objects with unknown motion patterns, in Proceedings of the ACM SIGMOD International Conference on Management of Data, 2004.
[43] Q. Li, Y. Zheng, X. Xie, Y. Chen, W. Liu, and W. Ma, “Mining user similarity based on location history, in Proceedings of ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, 2008.
[44] Z. Li, B. Ding, F. Wu, T. K. H. Lei, R. Kays, and M. Crofoot, “Attraction and avoidance detection from movements, Proceedings of the VLDB Endowment, vol. 7, no. 3, 2013.
[45] M. Andersson, J. Gudmundsson, P. Laube, and T. Wolle, “Reporting leaders and followers among trajectories of moving point objects, GeoInformatica, vol. 12, no. 4, 2008.
[46] T. F. Smith and M. S. Waterman, “Comparison of biosequences, Advances in applied mathematics, vol. 2, no. 4, 1981.
[47] Z. Li, F. Wu, and M. Crofoot, “Mining following relationships in movement data, in Proceedings of IEEE International Conference on Data Mining (ICDM), 2013.
[48] K. Chen and K. Chao, “On the range maximum-sum segment query problem, in International Symposium on Algorithms and Computation (ISAAC), 2004.
[49] F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi, “Trajectory pattern mining, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007.
[50] T. Bittner, “Rough sets in spatio-temporal data mining, in Proceedings of International Workshop on Temporal, spatial, and spatio-temporal data mining, 2000.
[51] A. R. Ganguly and K. Steinhaeuser, “Data mining for climate change and impacts, in Workshops Proceedings of IEEE International Conference on Data Mining (ICDM), 2008.
[52] D. Birant and A. Kut, “St-dbscan: An algorithm for clustering spatial-temporal data, Data ＆ Knowledge Engineering, vol. 60, no. 1, 2007.
[53] R. Trasarti, F. Pinelli, M. Nanni, and F. Giannotti, “Mining mobility user pro les for car pooling, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011.
[54] G. Andrienko, D. Malerba, M. May, and M. Teisseire, “Mining spatio-temporal data, Journal of Intelligent Information Systems, vol. 27, no. 3, 2006.
[55] J. L. Mennis and J. W. Liu, “Mining association rules in spatio-temporal data: An analysis of urban socioeconomic and land cover change, Transactions in GIS, vol. 9, no. 1, 2005.
[56] J. Gudmundsson and M. J. van Kreveld, “Computing longest duration ocks in trajectory data, Proceedings of ACM GIS International Symposium on Geographic Information Systems, 2006.
[57] H. Jeung, M. L. Yiu, X. Zhou, C. S. Jensen, and H. T. Shen, “Discovery of convoys in trajectory databases, Proceedings of the VLDB Endowment, vol. 1, no. 1, 2008.
[58] Z. Li, B. Ding, J. Han, and R. Kays, “Swarm: Mining relaxed temporal moving object clusters, Proceedings of the VLDB Endowment, vol. 3, no. 1, 2010.
[59] M. M. Deza and E. Deza, “Encyclopedia of distances, in Encyclopedia of Distances, 2009.
[60] J. Shin, D. Shin, and D. Shin, “Predicting of abnormal behavior using hierarchical markov model based on user pro le in ubiquitous environment, in Proceedings of International Conference on Grid and Pervasive Computing (GPC), 2013.
[61] L. Bergroth, H. Hakonen, and T. Raita, “A survey of longest common subsequence algorithms, in Proceedings of International Symposium on String Processing and Information Retrieval (SPIRE), 2000.
[62] T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu, “An e cient k-means clustering algorithm: Analysis and implementation, IEEE Transactions on Pattern Analysis ＆ Machine Intelligence, vol. 24, no. 7, 2002.
[63] L. Rokach and O. Maimon, “Clustering methods, in Data mining and knowledge discovery handbook, 2005.
[64] D. Reynolds, “Gaussian mixture models, Encyclopedia of biometrics, 2015.
[65] T. Hengl, G. B. Heuvelink, M. P. Tadi ́c, and E. J. Pebesma, “Spatio-temporal prediction of daily temperatures using time-series of modis lst images, Theoretical and Applied Climatology, vol. 107, 2012.
[66] S. Scellato, M. Musolesi, C. Mascolo, V. Latora, and A. T. Campbell, “Nextplace: A spatio-temporal prediction framework for pervasive systems, in Pervasive, 2011.
[67] ——, “Nextplace: a spatio-temporal prediction framework for pervasive systems, in Proceedings of International Conference on Pervasive Computing, 2011.
[68] F. Giannotti, M. Nanni, D. Pedreschi, and F. Pinelli, “Trajectory pattern mining, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007.
[69] E. Masciari, G. Shi, and C. Zaniolo, “Sequential pattern mining from trajectory data, in Proceedings of International Database Engineering ＆ Applications Symposium, 2013.
[70] R. Agrawal and R. Srikant, “Fast algorithms for mining association rules in large databases, in Proceedings of the VLDB Endowment, 1994.
[71] E. Schubert, A. Zimek, and H.-P. Kriegel, “Geodetic distance queries on r-trees for indexing geographic data, in Proceedings of International Symposium on Spatial and Temporal Databases, 2013.
[72] S. Skyum, “A simple algorithm for computing the smallest enclosing circle, Information Processing Letters, 1991.
[73] L. Backstrom and J. Leskovec, “Supervised random walks: predicting and recommending links in social networks, in Proceedings of International Conference on Web Search and Web Data Mining (WSDM), 2011.
[74] D. Liben-Nowell and J. M. Kleinberg, “The link prediction problem for social networks, in Proceedings of the ACM CIKM International Conference on Information and Knowledge Management, 2003.
[75] L. Lu and T. Zhou, “Link prediction in complex networks: A survey, Physica A: Statistical Mechanics and its Applications, vol. 390, no. 6, 2011.
[76] R. Cheng, J. Pang, and Y. Zhang, “Inferring friendship from check-in data of locationbased social networks, in Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2015.
[77] S. Scellato, A. Noulas, and C. Mascolo, “Exploiting place features in link prediction on location-based social networks, in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011.
[78] D. Wang, D. Pedreschi, C. Song, F. Giannotti, and A.-L. Barabasi, “Human mobility, social ties, and link prediction, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011.
[79] H. Zhuang, J. Tang, W. Tang, T. Lou, A. Chin, and X. Wang, “Actively learning to infer social ties, Data Mining and Knowledge Discovery, vol. 25, no. 2, 2012.
[80] N. Barbieri, F. Bonchi, and G. Manco, “Who to follow and why: link prediction with explanations, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014.
[81] X. Xiao, Y. Zheng, Q. Luo, and X. Xie, “Inferring social ties between users with human location history, Journal of Ambient Intelligence and Humanized Computing, vol. 5, no. 1, 2014.
[82] S. Wasserman and K. Faust, Social network analysis: Methods and applications. Cambridge University Press, 1994, vol. 8.
[83] M. D. Choudhury, W. A. Mason, J. M. Hofman, and D. J. Watts, “Inferring relevant social networks from interpersonal communication, in Proceedings of International Conference on World Wide Web (WWW), 2010.
[84] N. Eagle, A. S. Pentland, and D. Lazer, “Inferring friendship network structure by using mobile phone data, Proceedings of the National Academy of Sciences, vol. 106, no. 36, 2009.
[85] S. Myers and J. Leskovec, “On the convexity of latent social network inference, in Proceedings of International Conference on Neural Information Processing Systems, 2010.
[86] D. J. Crandall, L. Backstrom, D. Cosley, S. Suri, D. Huttenlocher, and J. Kleinberg, “Inferring social ties from geographic coincidences, Proceedings of the National Academy of Sciences, vol. 107, no. 52, 2010.
[87] C. Wang, M. Ye, and W. Lee, “From face-to-face gathering to social structure, in Proceedings of ACM CIKM International Conference on Information and Knowledge Management, 2012.
[88] E. Cho, S. A. Myers, and J. Leskovec, “Friendship and mobility: user movement in location-based social networks, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011.
[89] X. Liu, Q. He, Y. Tian, W. Lee, J. McPherson, and J. Han, “Event-based social networks: linking the online and o ine social worlds, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012.
[90] P. Yin, Q. He, X. Liu, and W.-C. Lee, “It takes two to tango: Exploring social tie development with both online and o ine interactions, in Proceedings of SIAM International Conference on Data Mining (SDM), 2014.
[91] O. J. Mengshoel, R. Desai, A. Chen, and B. Tran, “Will we connect again? machine learning for link prediction in mobile social networks, in Proceedings of Intertional Workshop on Mining and Learning with Graphs, 2013.
[92] K. Chen, J. Han, and Y. Li, “HALLP: A hybrid active learning approach to link prediction task, Journal of Computers, vol. 9, no. 3, 2014.
[93] H. Bagci and P. Karagoz, “Context-aware friend recommendation for location based social networks using random walk, in Proceedings of International Conference on World Wide Web (WWW), 2016.
[94] J. Li, F. Xia, W. Wang, Z. Chen, N. Y. Asabere, and H. Jiang, “Acrec: a co-authorship based random walk model for academic collaboration recommendation, in Proceedings of International Conference on World Wide Web (WWW), 2014.
[95] S. Liu, B. Wang, and M. Xu, “Event recommendation based on graph random walking and history preference reranking, in Proceedings of ACM SIGIR International Conference on Research and Development in Information Retrieval, 2017.
[96] H. Bagci and P. Karagoz, “Context-aware location recommendation by using a random walk-based approach, Knowledge and Information Systems, vol. 47, no. 2, 2016.
[97] J. J. Ying, W. Kuo, V. S. Tseng, and E. H. Lu, “Mining user check-in behavior with a random walk for urban point-of-interest recommendations, ACM Transactions on Intelligent Systems and Technology, vol. 5, no. 3, 2014.
[98] Y. Sun, J. Han, X. Yan, P. S. Yu, and T. Wu, “Pathsim: Meta path-based top-k similarity search in heterogeneous information networks, Proceedings of the VLDB Endowment, 2011.
[99] G. Jeh and J. Widom, “Simrank: a measure of structural-context similarity, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002.
[100] H. Tong, C. Faloutsos, and J. Pan, “Fast random walk with restart and its applications, in Proceedings of IEEE International Conference on Data Mining (ICDM), 2006.
[101] J. Pan, H. Yang, C. Faloutsos, and P. Duygulu, “Automatic multimedia cross-modal correlation discovery, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004.
[102] J. Sun, H. Qu, D. Chakrabarti, and C. Faloutsos, “Neighborhood formation and anomaly detection in bipartite graphs, in Proceedings of the 5th IEEE International Conference on Data Mining, 2005.
[103] H. Tong and C. Faloutsos, “Center-piece subgraphs: problem de nition and fast solutions, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006.
[104] B. Settles, “Active learning literature survey, University of Wisconsin, Madison, vol. 52, no. 55-66, p. 11, 2010.
[105] M. McPherson, L. Smith-Lovin, and J. M. Cook, “Birds of a feather: Homophily in social networks, Annual Review of Sociology, vol. 27, no. 1, 2001.
[106] L. Manikonda, Y. Hu, and S. Kambhampati, “Analyzing user activities, demographics, social network structure and user-generated content on Instagram, CoRR, vol. abs/1410.8099, 2014.
[107] M. Redi, D. Quercia, L. T. Graham, and S. D. Gosling, “Like partying? your face says it all. predicting the ambiance of places with pro le pictures, in Proceedings of International Conference on Web and Social Media (ICWSM), 2015.
[108] F. Souza, D. de Las Casas, V. Flores, S. Youn, M. Cha, D. Quercia, and V. Almeida, “Dawn of the sel e era: the whos, wheres, and hows of sel es on Instagram, in Proceedings of ACM Conference on Online Social Networks, 2015.
[109] K. Feng, G. Cong, S. S. Bhowmick, and S. Ma, “In search of in uential event organizers in online social networks, in Proceedings of the ACM SIGMOD International Conference on Management of Data, 2014.
[110] K. Li, W. Lu, S. Bhagat, L. V. S. Lakshmanan, and C. Yu, “On social event organization, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014.
[111] Y. Fujiwara, M. Nakatsuji, M. Onizuka, and M. Kitsuregawa, “Fast and exact top-k search for random walk with restart, Proceedings of the VLDB Endowment, vol. 5, no. 5, 2012.
[112] E. Cho, S. A. Myers, and J. Leskovec, “Friendship and mobility: user movement in location-based social networks, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011.
[113] M. Hay, G. Miklau, D. D. Jensen, D. F. Towsley, and C. Li, “Resisting structural reidenti cation in anonymized social networks, The International Journal on Very Large Data Bases, 2010.
[114] K. Liu and E. Terzi, “Towards identity anonymization on graphs, in Proceedings of the ACM SIGMOD International Conference on Management of Data, 2008.
[115] C. Sun, P. S. Yu, X. Kong, and Y. Fu, “Privacy preserving social network publication against mutual friend attacks, Transactions on Data Privacy, vol. 7, no. 2, pp. 71–97, 2014.
[116] C.-H. Tai, P. S. Yu, D.-N. Yang, and M.-S. Chen, “Privacy-preserving social network publication against friendship attacks, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011.
[117] Y. Wang and B. Zheng, “Preserving privacy in social networks against connection ngerprint attacks, in Proceedings of IEEE ICDE International Conference on Data Engineering, 2015.
[118] B. Zhou, J. Pei, and W. Luk, “A brief survey on anonymization techniques for privacy preserving publishing of social network data, SIGKDD Explorations, 2008.
[119] G. Acs and C. Castelluccia, “A case study: Privacy preserving release of spatio-temporal density in paris, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014.
[120] M. E. Andr ́es, N. E. Bordenabe, K. Chatzikokolakis, and C. Palamidessi, “Geoindistinguishability: Di erential privacy for location-based systems, in Proceedings of ACM SIGSAC Conference on Computer and Communications Security, 2013.
[121] N. E. Bordenabe, K. Chatzikokolakis, and C. Palamidessi, “Optimal geo-indistinguishable mechanisms for location privacy, in Proceedings of ACM SIGSAC Conference on Computer and Communications Security, 2014.
[122] D. J. Mir, S. Isaacman, R. C ́aceres, M. Martonosi, and R. N. Wright, “Dp-where: Di erentially private modeling of human mobility, in Proc. of IEEE Big Data, 2013.
[123] K. P. Puttaswamy, S. Wang, T. Steinbauer, D. Agrawal, A. El Abbadi, C. Kruegel, and B. Y. Zhao, “Preserving location privacy in geosocial applications, IEEE Transactions on Mobile Computing, 2014.
[124] M. Backes, M. Humbert, J. Pang, and Y. Zhang, “walk2friends: Inferring social links from mobility pro les, in Proceedings of ACM SIGSAC Conference on Computer and Communications Security, 2017.
[125] D. Pisinger, “Upper bounds and exact algorithms for p-dispersion problems, Computers ＆ OR, vol. 33, 2006.
[126] H. Pham, L. Hu, and C. Shahabi, “Towards integrating real-world spatiotemporal data with social networks, in Proceedings of ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems, 2011.
[127] A. Likhyani, S. Bedathur, and D. P, “Locate: In uence quanti cation for location promotion in location-based social networks, in Proceedings of International Joint Conference on Arti cial Intelligence (IJCAI), 2017.
[128] A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks, in Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016.
[129] A. Noulas, S. Scellato, N. Lathia, and C. Mascolo, “A random walk around the city: New venue recommendation in location-based social networks, in Proceedings of International Conference on Privacy, Security, Risk and Trust and International Conference on Social Computing, 2012.