NUI Galway – UL Alliance First Annual ENGINEERING AND - ARAN ...

User Similarity and Interaction in Online Social Networks
Abstract
Online social networks like facebook and twitter are
defined by connections between users, and by
interactions between users. We analyse OSN user
connections and interactions in novel ways to gain
greater insight into the complex dynamics of these
networks, and into the users of such networks.
We uncover hidden graphs in such networks and
develop new methods of analyzing these graphs, with
direct implications for better social search and
recommender systems algorithms. We conduct new
forms of empirical analysis into OSN data to analyse
user similarity, combining this with social graph
analysis for a new perspective on the ‘birds of a
feather’ phenomenon in OSNs. We also investigate what
happens to user similarity as a function of network
distance, with an associated novel graph-generation
simulation mechanism to build social graphs based
partially on similarities between users, and not merely
on the traditional model of preferential attachment.
1. A weighting mechanism for interaction
graphs
There are hidden graphs inside the overt social graph
described by ‘follower’/’following’ relationships in
OSNs, notably the graph described by user interactions:
posting on friends’ facebook walls, replying to other
users etc. These interaction graphs are potentially more
useful in discerning the true nature and strength of ties
between users, and are thus valuable in social search
algorithms and recommenders.
An effective weighting mechanism is necessary for
effective analysis of these graphs. Such a mechanism
should incorporate normalization for the varying levels
of a user’s interactions, the frequency with which a user
interacts with a particular other user, and temporal
aspects to reflect strength for more recent interactions.
Practical computational aspects should also be
considered for maintenance of these highly dynamic
graphs. We are developing a weighting mechanism for
dealing with these issues, which can likely be applied to
interaction graphs in other areas such as biological
networks.
2. “Birds of a feather” in twitter data
To gain an empirical measure of similarity between a
set of twitter users, we create a document for each user
comprised of all of their aggregated posts. We convert
John Conroy
Colm O’Riordan, Josephine Griffith
j.conroy3@nuigalway.ie
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these documents to n-dimensional vectors, using tf-idf
weighting for each of the unique terms present in the
document. In this way we convert documents to vectors
of equal dimension, which can be compared for a
measure of similarity in vector space.
We compare these vectors using cosine similarity to
attain an empirical measure of similarity between any
two users.
Using this metric, we analyse user similarity based
on various graph-based criteria. We find that users who
are linked in the overt social graph are more similar than
those who are unlinked: birds of a feather do indeed
flock together. We analyse similarity in a time-slice
analysis, and in light of the second-order social graph
comprised of user interactions, and in other ways.
3. User similarity as a function of network
distance
Nodes in a graph lie at a network distance of one if
they are directly linked, at a network distance of two if
they share a common friend, a distance of three if they
are two nodes removed in the graph, and so on. Work
by ourselves and others[1] shows that users at a network
distance of one are more similar than users who are not
directly linked, but the relationships between similarities
over higher network distances remains an open
question.
We hypothesise that similarity between users as a
function of network distance decays in some predictable
logarithmic fashion, asymptotic towards some lower
bound dictated by the properties of the network.
Knowledge of this similarity distribution and lower
bound would likely have applications in fine-grained
recommender systems and social search algorithms.
Our approach to uncovering these network properties
is simulation: we create network nodes with an array of
p parameters, representing node attributes (in an OSN
like facebook these correspond to use characteristics
like user age, location, career area, hobbies etc.). We
then build the network graph iteratively, adding edges
based on the traditional preferential attachment model,
augmented by similarity between users. We analyse the
networks so-generated to investigate node (user)
similarity as a function of network distance.
References
[1] Mislove, A, Marcon, M, Gumaddi, K, Drushel, P,
Bhattachargee, B, Measurement and analysis of online social
networks, Proceedings of the 7th ACM SIGCOMM
conference on Internet measurement