Using content and interactions for discovering communities in
IBM Research India
from a social network
We propose generative models that can
discover communities based on the discussed
topics, interaction types and the social
connections among people.
Person->multiple communities->multiple topics
We discover both community interests and user
interests based on the information and linked
rich data -> academia & business;
discover relationships -> discover community
community is a collection of users as a group
such that there is high relatedness among people
within the group.
One common approach used is to treat
communities as group of nodes in social network
that are more densely connected among
themselves than with the rest of the network.
A graph clustering problem
consider communities as “groups of
users(nodes) who are interconnected and
communicate on shared topics”.
We also utilize the “type” of interactions
between users to emphasize their interest in
topics, and thus community membership.
e.g, conversation vs broadcast
两种社交网络：1. 用户的posts 广播给他的邻居；2.
点特性和user interactions. 不允许一个user属
第二种：Bayesian probabilistic models . 可
Communities are modeled as random
mixtures over users who in turn have a
topical distribution (interest) associated
with them. 没有利用链接信息。
Twitter over a period of six months in 2009
Enron Email corpus
set the number of communities C at
10 and topics Z at 20
We ran 1000 iterations to burn in and took
250 samples (every fourth sample) in the
next 1000 iterations .
proposed probabilistic schemes that
incorporate topics, social relation ships
and nature of posts for more effective
community discovery .
Interaction types are important
communities in incomplete information
networks with missing edges.
1. learn a distance metric to reproduce the linkbased distance between nodes from the
observed edges in the local information regions
2. Use the learned distance metric to estimate the
distance between any pair of nodes in the
A hierarchical clustering approach
community is deﬁned as a group of nodes
which are densely connected inside the group,
while loosely connected with the nodes outside
The local regions with complete linkage
information are called local information regions .
We identify and deﬁne the problem of community
detection in incomplete information networks with
local information regions
Then a metric, which can be used to measure the
distance between any pair of nodes, is learned.
Based on the learned metric, we devise a
distance-based modularity function to evaluate
the quality of the communities.
We propose a distance-based algorithm DSHRINK
which can discover the hierarchical and
focused on the topological structures
Some graph clustering methods which
based on attributes.
some clustering methods based on both
links and attributes were also proposed
up the Clustering Process with
DBLP-A Dataset: DBLP-A is the data set
extracted from DBLP database which provides
bibliographic information on computer
science journals and proceeding.
Information Network Generation
p ,called sample ratio
parameter q ,called local information region
The deﬁnition of purity is as follows:
each cluster is ﬁrst assigned with the most
frequent class in the cluster, and then the
purity is measured by computing the
number of the instances assigned with the
same labels in all clusters.
Md +DSHRINK: We learn a diagonal
Mahalanobis matrix Md and use it as the
input of M for DSHRINK.
Mf +DSHRINK: We learn a full Mahalanobis
matrix Mf and use it as the input of M for