2015/10/111 dbconnect: mining research community on dblp data osmar r. zaïane, jiyang chen, randy...
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112/04/19 1
DBconnect: Mining Research Community on DBLP Data
Osmar R. Zaïane, Jiyang Chen, Randy Goebel
Web Mining and Social Network Analysis Workshop in conjunction with ACM SIGKDD, SNA-KDD'07
報告人 : 吳建良
Outline Community Motivation
Understand research community – recommend collaborations Proposed Apporach
Rank the relevance with a random walk approach DBconnect
A navigational system to investigate community relations Conclusion
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What is community? In Graph Theory:
Densely connected groups of vertices, with sparser connection between groups
In Social Network Analysis: Groups of entities that share
similar properties or connect to each other via certain relations
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Why is community important? Interesting data with community structure:
Researcher collaboration, friendship network, WWW,
Massive Multi-player on-line gaming, electronic
communications…
Groups in social networks correspond to social communities, which can be used to understand organizational structure, academic collaboration, shared interests and affinities, etc.
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Motivation
Understand the research network between authors,
conferences and topics (rank entities by relevance
for given entities)
Find and recommend research collaborators for
given authors
Explore the academic social network
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Proposed Approach
Build bipartite graph in the author-conference space
Limitation of traditional bipartite graph model
Extend the bipartite model to include co-authorship
information
Further extend the model to tripartite to include topic
information
Use random walk with restart on such models
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An example Author Publication Records in Conferences
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a, b, c, d, e are authors ac(3) means that author a and c published three papers together in
KDD(y) conference
Bipartite model for conference-author social network
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Weight(edge)=publishing frequency of author in a certain conferenceLimitation:Fail to represent any co- co-authorships
To capture the co-author relations:1.Add a link between a and c miss the role of KDD2.Make the link connecting a and c to KDD make the random walk infeasible3.Add additional nodes to represent each co-author relation impractical, a huge number of such relations
Extend the bipartite model to include co-authorship information
Add a virtual level of nodes to replace the conference partition, and add direction to the edges
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3
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A nodes then connect to their own split
relation nodes with the original weight C’ nodes to all author nodes
If the A node and C’ node have a co-author
relation edge weight: co-author
frequency * a parameter f
Otherwise, the edge is weighted as original
Set f=k (k is the total author number of
a conference)
3f
3f
3
77
7
7
3 7
Further extend the model to tripartite to include topic information
Research topic is an important component to differentiate any research community
Authors that attend the same conferences might work on various topics
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Adding topic information Very few conference proceedings have their table of
contents included in DBLP Table of contents include session titles
Extract relevant topics from DBLP Use paper title, and find frequent co-locations in title text
Method Manually select a list of stopwords to remove frequently
used but non-topic-related words
Ex: Towards, Understanding, Approach, … 11
Adding topic information (cond.)
Count frequency of every co-located pairs of stemmed words
Select the top 1000 most frequent bi-grams as topics Manually add several tri-grams
Ex: World Wide Web, Support Vector Machine, …
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Random walk on DBLP social network
Problem to be solving: Given an author node a A , compute a relevance score for
each author b A Simple example: conference-author network G
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Relational matrix M3×5
Random walk on DBLP social network (cond.)
Normalize M such that every column sum up to 1: Q(M) = col_norm(M), Q(MT) = col_norm(MT)
Construct the adjacency matrix J of G after normalization
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0)(
)(0TMQ
MQJ
22.00.108.00
77.000.1038.0
0002.062.0
)(MQ
22.041.00
33.000
041.00
44.0016.0
018.084.0
)( TMQ
Random walk on DBLP social network (cond.)
Normalized adjacency matrix J of G
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Q(MT )
Q(M )
A random walk on this graph moves from one node to one of its neighbors based on the probability Probability: proportional to the weight of the edge over the
sum of weights of all edges that connect to this node EX: if we start from node SIGMOD, then build u as
the start vector u is a one-column vector, consisting of (3+7) elements The value of element corresponding to SIGMOD is set to 1
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Random walk on DBLP social network (cond.)
u=Ju After step1 of the first iteration, the random walk hits
the author nodes with b=1×0.44, d=1×0.33, e=1×0.22
After step2 of the first iteration, the chance that the random walk goes back to SIGMOD is 0.44×0.8+0.33 ×1+0.22 ×0.22 = 0.73, and the other 0.27 goes to the other two conference nodes
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Random walk on DBLP social network (cond.)
After a few iterations, the vector will converge and gives a stable score to every node
However, these scores are always the same no matter where the walk begins
Solved by random walk with restart Given a restarting probability c Use another vector v, and the value of element corresponding
to SIGMOD is set to 1 In each random walk iteration, the walker goes back to the
start node with a restart probability18
Random walk on DBLP social network (cond.)
u=(1-c)u + cv
Random walk with restart algorithm(1)
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Random walk on DBLP social network (cond.)
Input: node α A, a bipartite graph model G, restarting probability c, converge threshold ε.Output: relevance score vector B for author nodes.1. Compute the adjacency matrices J(n+m) ×(n+m) of G. /* n conferences and m authors */2. Initialize vα = 0, set element for α to 1: vα(α) = 1.3. While (△uα > ε ) uα = Juα
uα = (1 − c) uα + cvα
4. Set vector B = uα(n+1:n+m).5. Return B.
Extend the bipartite model into a directed bipartite graph G'=(C',A,E') A has m author nodes, and C has n conference nodes C' is generated based on C and has n*m nodes
Assume every node in C is split into m nodes
First generate a matrix M(n*m)×m for directional edges from C' to A
Then form a matrix Nm×(n*m) for edges from A to C'
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Random walk on DBLP social network (cond.)
The adjacency matrix J of G‘
Algorithm(2): The random walk with restart algorithm for directed bipartite model
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Random walk on DBLP social network (cond.)
Extend to the tripartite graph model G''=(C,A,T,E'') Assume n conferences, m authors and l topics in G'‘
Three corresponding matrices: Un×m, Vm×l and Wn×l
The adjacency matrices of G'' after normalization:
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Random walk on DBLP social network (cond.)
Algorithm(3): The random walk with restart algorithm for tripartite model
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Random walk on DBLP social network (cond.)
DBLP dataset Download the publication data for conferences from
the DBLP website9 in July 2007 It contains more than 300,000 authors, about 3,000
conferences and the selected 1,000 N-gram topics The entire adjacency matrix becomes too big to make
the random walk efficient Use the METIS algorithm to partition the large graph into ten
subgraphs of about the same size
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The DBconnect System http://kingman.cs.ualberta.ca/research/demos/co
ntent/dbconnect/ A navigational system to investigate the
community connections and relations Displaying researcher statistics from academic
search engines Providing lists of recommended entities to given
authors, topics and conferences
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The DBconnect System (cond.)
Academic Information Conference contribution, earliest publication year and
average publication per year H-index is calculated based on information retrieved from
Google Scholar Approximate citation numbers
Related Conferences Based on author-conference-topic model
Related Topics Based on author-conference-topic model
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The DBconnect System (cond.)
Co-authors Co-author name and number of paper
Related Researchers Based on the directed bipartite graph model
Recommended Collaborators Based on author-conference-topic model Co-authors’ names are not shown here The result implies that the given author shares similar topics
and conference experiences with these listed researchers, hence the recommendation
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The DBconnect System (cond.)
Recommended To The recommendation is not symmetric Author A may be recommended as a possible future
collaborator to author B but not vice versa EX: Jiawei Han has been recommended as collaborator for
6201 authors, but apparently only a few of them is recommended as collaborators to him
The given author has been recommended to the author lists Symmetric Recommendations
The author lists have been recommended to the given author
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Conclusion Extend a bipartite graph model to incorporate
co-authorship Propose a random walk with restart approach
Find related conferences, authors, and topics for a given entity
Present DBconnect system Help explore the relational structure and discover
implicit knowledge within the DBLP data collection
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