The Co-Evolution Model for Social Network Evolving and
Opinion Migration
Yupeng Gu
University of California, Los Angeles
Los Angeles, CA
ypgu@cs.ucla.edu
Yizhou Sun
University of California, Los Angeles
Los Angeles, CA
yzsun@cs.ucla.edu
Jianxi Gao
Northeastern University
Boston, MA
j.gao@neu.edu
ABSTRACT
Almost all real-world social networks are dynamic and evolving
with time, where new links may form and old links may drop,
largely determined by the homophily of social actors (i.e., nodes
in the network). Meanwhile, (latent) properties of social actors,
such as their opinions, are changing along the time, partially due
to social inuence received from the network, which will in turn
aect the network structure. Social network evolution and node
property migration are usually treated as two orthogonal prob-
lems, and have been studied separately. In this paper, we propose
a co-evolution model that closes the loop by modeling the two
phenomena together, which contains two major components: (1)
a network generative model when the node property is known;
and (2) a property migration model when the social network struc-
ture is known. Simulation shows that our model has several nice
properties: (1) it can model a broad range of phenomena such as
opinion convergence (i.e., herding) and community-based opinion
divergence; and (2) it allows to control the evolution via a set of fac-
tors such as social inuence scope, opinion leader, and noise level.
Finally, the usefulness of our model is demonstrated by an applica-
tion of co-sponsorship prediction for legislative bills in Congress,
which outperforms several state-of-the-art baselines.
CCS CONCEPTS
•Information systems →Data mining;
KEYWORDS
Dynamic networks; network generation models; co-evolution
1 INTRODUCTION
Social network analysis has become prevalent as the variety and
popularity of information networks increase. In the real world, net-
works are evolving constantly with links joining and dropping over
time. Meantime, properties of social actors in these networks, such
as their opinions, are constantly changing as well. One example
is the political ideology migration for two parties in U.S. Figure 1
shows the 1-dimensional mean ideology for members in two politi-
cal parties via ideal point estimation using their historical voting
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DOI: hp://dx.doi.org/10.1145/3097983.3098002
records [
12
]. A similar discovery can be seen in [
2
]. We can clearly
observe the divergence of ideologies of the two communities (i.e.
the Democrats and Republicans), especially the polarization trend
since 1960s. A natural question raises, why such divergence happens
and is there any possible intervention we can have to alleviate such po-
larization? In this paper, we aempt to interpret this phenomenon
and thus propose a unied co-evolution model for link evolution
as well as (latent) node property migration in social networks.
Figure 1: Ide ology migration of the two parties in U.S.
On one hand, people in social networks exhibit great diversity
and are associated with dierent properties (e.g., hidden properties
such as political ideology). Interactions between individuals are
more likely to happen within people that are alike, described as
“homophily” in social network analysis [
28
]. With this principle,
network generative models such as blockmodels [
18
,
44
] and latent
space models [
17
] have emerged, where each individual is assigned
with a feature vector denoting her latent properties (i.e., a position
in a latent space). Individuals that are close in the latent space are
likely to have interactions in the network.
On the other hand, like ocks of collectively moving animals,
people in social networks comprise a system of interacting, perma-
nently moving units. In fact, the changing of location is ubiquitous
among many kinds of creatures in real life: ocks of birds y and
migrate; colonies of ants and drones work and move to seek for
foods. is phenomenon is also overwhelming in the realm of
social network analysis, where people’s latent position (e.g., ideol-
ogy) are migrating with their crowds (e.g., parties). In other words,
individuals are likely to be aected by their friends or who they
interact with in the social network. is “social inuence” [
22
,
41
]
assumption has been widely applied in literature. For example,
in an information diusion model, a person will be activated (i.e.
the binary status is switched to “on”) if she has enough activated
neighbors [14].
Inspired by these observations, in this paper we propose a proba-
bilistic co-evolution model that explains the evolution of networks
KDD 2017 Research Paper
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175