Tutorial, IEEE SERVICE 2014 Anchorage, Alaska

Transcription

1 Tutorial, IEEE SERVICE 2014 Anchorage, Alaska Big Data Science: Fundamental, Techniques, and Challenges (Data Mining on Big Data) By Neil Y. Yen Presented by Incheon Paik University of Aizu Japan Data Mining on Big Data 1

2 Background Data everywhere Growth of communication channels s (10-20 times / day) messenger (3-4 hours / day) social media (80% of a day) and coming Power of social media an integrated portal to interact with Changes on human behaviors information-sharing, experience crowdsourcing, and knowledge cultivation Note. Statistics summarized from IDC at 2

3 Background Data everywhere Wikipedia ( 30 million pages ; 80 million edits ) YouTube ( 100 million videos ; 150 million accesses ) Blogosphere ( 250 million blogs ; 500 million views ) number of posts are decreasing Twitter ( 30 billion tweets ; 5 million shares ) Facebook ( 900 million objects ; 250 million uploads ) Yahoo Answer / 知 恵 袋 ( 1.7 billion questions ; 900 million answers ) Flickr ( 5 billion photos) * 22% in 3.2 billions of Internet Users Note. Statistics summarized from IDC at Last update on January 01,

4 Background Data everywhere 4

5 Background Data everywhere in general case Platform, Person-oriented, Comprehensive Entry, Industry-oriented, Focus Note. Statistics summarized from IDC at 5

6 More Data More Information ( a foreseeable growth of the Internet and media ) More Data More Complexity ( scalability, integrity, consistency of data ) More Data More Heterogeneity ( different methods for data processing ) 6

7 Part I: Data Mining on Social network Introduction to social network Emerging models for social network Mining the social network(s) 7

8 Introduction to social network Primary participants Individuals (or objects) Node(s) Connections (or correlations) Link(s) Social networks can be interpreted as phenomenon derived by individuals with diverse interactions among them. The Small World: Six degrees of separation by S. Milgram (1967) 8

9 Introduction to social network Primary participants Individuals (or objects) Node(s) Connections (or correlations) Link(s) From another point of view, the Earth is an electronic nervous system, implementing by a conceptual network with nodes and links: nodes such as laptops, smartphones, satellites, etc. links such as cable lines, signals, etc. that make the node connected Communication networks: Many non-identical components with diverse connections between them 9

10 Introduction to social network Consider many kinds of networks: social, technological, business, economic, content, These networks tend to share certain informal properties: large scale, continual growth distributed, democratic growth: vertices decide who to link to mixture of local and long-distance connections abstract notions of distance: geographical, content, social, Main concerns Do natural networks share quantitative universals? What would these universals be? How can they be well modeled, analyzed, and explained? All the phenomenon follows the theories of social network, and can always be easily explained through link analysis 10

11 Introduction to social network Connected participants: how many, and how large Network diameter: maximum (worst-case) or average exclude infinite distances? (disconnected components) the small-world phenomenon Clustering: to what extent that links tend to cluster locally what is the balance between local and long-distance connections what roles do the two types of links play Degree distribution: what is the typical degree in the network what is the overall distribution 11

12 Introduction to social network Probabilistic and/or statistical models towards well management the generation of network(s) Various parameters to be concerned: network size degree of vertex the connections Statements are always statistical in nature: with high probability, diameter is small on average, degree distribution has heavy tail 12

13 Part I: Data Mining on Social network Introduction to social network Emerging models for social network Mining the social network(s) 13

14 Emerging models for social network Random graphs Erdös-Rényi model (1960): Few components and small diameter No high clustering and heavy-tailed degree distributions A well-studied and understood mathematical model in general case Random graphs Watts & Strogatz model (1998): Few components, small diameter and high clustering No heavy-tailed degree distributions Scale-free Networks: Few components, small diameter and heavy-tailed distribution No high clustering Hierarchical networks: Few components, small diameter, high clustering, heavy-tailed 14

15 Emerging models for social network Case I: The Internet Nodes: computers, routers Links: physical lines cluster end device connector link 15

16 Emerging models for social network Case II: The Actor Network Nodes: actors Links: the rest casts conceptual connected Flatliners (1990) A Few Good Men (1992) Sleepers (1996) connected The River Wild (1994) Apollo 13 (1995) In the Cut (2003) 16

17 Emerging models for social network Case III: Co-authorship Network Nodes: authors Links: coauthor/coedit on academic publications Retrieved from Microsoft Academic Search 17

18 Emerging models for social network Case IV: Academic Citation Network Nodes: authors Links: cite academic publications Retrieved from Microsoft Academic Search 18

19 Emerging models for social network Case V: Food Network R.J. Williams, N.D. Martinez Nature (2000) Nodes: trophic species Links: interactions among selected trophic species Case VI: Food Network Liljeros et al. Nature (2001) Nodes: human beings categorized by sexual property Links: sexual relationships You can find many kinds of network structure as long as interactions existing among potential participants. 19

20 Part I: Data Mining on Social network Introduction to social network Emerging models for social network Mining the social network(s) 20

21 Mining the social network(s) Heterogeneous, multi-relational data represented as a graph Nodes as objects Heterogeneous objects need to be concerned Attributes of objects matter Considering sub-classes of objects and their corresponding labels Edges as links Different types of link may exist on same graph Weighted graph, dual-weighted graph, or others Links represent relationships and interactions between objects All we expect to know is the meaning of links Understanding the meaning(s) of link can help identify the relationship between objects 21

22 Mining the social network(s) Conventional approaches applied in machine learning and data mining consider that a random sample of homogeneous objects from single relation However, the real-world datasets are supposed to be multi-relational, heterogeneous, and semi-structured, which are totally different from the traditional assumptions So, the link mining represents an emerging field of research that concentrates the intersection of network and link analysis, hypertext and web mining, graph mining, relational learning and inductive logic programming Simply speaking, it is a multi-disciplinary field of study although most of its core concepts are derived from the existing methods. 22

23 Mining the social network(s) taxonomy of link mining Object-Related Tasks Link-based object ranking Link-based object classification Object clustering (group detection) Object identification (entity resolution) Link-Related Tasks Link prediction Link re-construction Link understanding 23

24 Mining the social network(s) methods to link mining Properties: Scale free [Barabasi 99], Clustering [Watts-Strogatz 98], Navigation [Adamic- Adar 03, LibenNowell 05], Bipartite cores [Kumar et al. 99], Network Motifs [Milo et al. 02], Communities [Nawman 99], Conductance [Mihail-Papadimitriou 06], Hub and authorities [Page et al. 98, Kleinberg 99] PageRank [Page et al. 99], Hyperlink-Induced Topic Search [Kleinberg 99], EigenRumor [Fujimura 05] Models: Preferential attachment [Barabasi 99], Small-world [Watts-Strogatz 98], Copying model [Kleinberg et al. 01], Heuristically tradeoffs [Fabrikant et al. 02], Congestion [Mihail et al. 03], Searchability [Kleinberg 02], Bowtie [Broder et al. 00], Transitstub [Zegura 97], Jellyfish [Tauro et al. 01] Path-Oriented: Neighborhood Selection, Swarm Intelligence Efficiency-Oriented: Greedy approach, SSON (Semantic Social Overlay Network), ESLP (Efficient Social-like Peer-to-peer network) 24

25 Mining the social network(s) Link is defined as the relationship among data Two kinds of linked networks homogeneous vs. heterogeneous Homogeneous networks Single object type and single link type Single model social networks (e.g., friends) WWW: a collection of linked Web pages Heterogeneous networks Multiple object and link types Medical network: patients, doctors, disease, contacts, treatments Bibliographic network: publications, authors, venues 25

26 Mining the social network(s) Link-based Ranking is primarily to exploit the link structure in a graph and to order or prioritize the set of objects within the graph Web information analysis PageRank and HITS are typical approaches inspired by link-based ranking Link-based ranking is considered a core technique in mining the network structure (so as in social network analysis) It is applied to rank participants in terms of centrality Degree centrality vs. Eigen vector/power centrality Rank objects relative to one or more relevant objects in the graph vs. ranks object over time in dynamic graphs 26

27 Mining the social network(s) the PageRank Algorithm by Brin & Page (1998) PageRank is essentially citation counting, but improves over simple counting Considering the indirect citations Smoothing of citations PageRank can also be interpreted as random surfing P(B) P(C) referring to P(A) A B C D deriving from P(D) E F 27

28 Mining the social network(s) the PageRank Algorithm by Brin & Page (1998) Random surfing model: at any page, With prob., randomly jumping to a page With prob. (1 ), randomly picking a link to follow d 3 d 1 M 0 0 1/ 2 1/ / 2 1/ Transition matrix Same as /N d 2 d 4 1 p ( d ) (1 ) m p ( d ) p ( d ) t 1 i ji t j t k d IN ( d ) k N 1 p( di ) [ (1 ) mki ] p( dk ) N T p ( I (1 ) M ) p k j i I ij = 1/N Stationary ( stable ) distribution, so we ignore time Initial value p(d)=1/n Iterate until converge 28

29 Mining the social network(s) Another model, link-based classification, is to predict the category of an object based on its attributes, links and the attributes of correlated objects among graph(s) Here we may need to take the multi-modal, multi-layered graph and their corresponding attributes to design the methods for link and object mining Web: Predict the category of a web page, based on words that occur on the page, links between pages, anchor text, html tags, etc. Citation: Predict the topic of a paper, based on word occurrence, citations, co-citations Communication: Predict whether a communication contact is by , phone call or mail 29

30 Mining the social network(s) Group detection Cluster the nodes in the graph into groups that share common characteristics Web Identifying communities Citation identifying research communities Entity resolution To predict when two objects are the same, based on their attributes and their links Web predict when two sites are mirrors of each other Citation predicting when two citations are referring to the same paper Epidemics predicting when two disease strains are the same or similar Biology learning when two names refer to the same protein 30

31 Mining the social network(s) Methods in entity resolution was taken as pair-wise resolution problem: resolved based on the similarity of their attributes (i.e., association rule or model in data mining) All these methods consider the importance on links Links in entity resolution Collective resolution: one resolution decision affects another if connection exists among them Probabilistic models interact with different entity recognition decisions 31

32 Mining the social network(s) link prediction Predict whether the relationship exists between two participants in graph based on attributes and all correlated links Web: predict if there will be a link between two pages Citation: predicting if a paper will cite another paper Epidemics: predicting who a patient s contacts are Applied Methods Often viewed as a binary classification problem Local conditional probability model, based on structural and attribute features Difficulty: sparseness of existing links Collective prediction, e.g., Markov random field model 32

33 Mining the social network(s) link estimation Make prediction to the number of links of a connected participant Web: predict the authority of a page based on the number of in-links; identifying hubs based on the number of out-links Citation: predicting the impact of a paper based on the number of citations Epidemics: predicting the number of people that will be infected based on the infectiousness of a disease Make prediction to the number of participants reachable by a given participant Web: predicting number of pages retrieved by crawling a site Citation: predicting the number of citations of a particular author in a specific journal 33

34 Conclusion Big Data Big Opportunity? or Big Problem? What is your target or subjective? How will it be done? but do not forget the human Making Balance is a challenging issue Infrastructure (storage), Management (governance, analysis), Search (value discovery), Security (transparency v.s. privacy), Applications (human-centered) Next? building the strategic alliances industry, academia, and government worldwide making opportunities before intending to find them (think before acting, and sometimes act before thinking) 34