The document proposes a visualization tool called SFViz to explore and recommend friends in social networks by considering both social connections and user interests. SFViz extracts user interest information from tags, constructs tag networks, calculates similarities between users based on tag networks and social networks, and generates a compound graph for visualization. SFViz uses a radial, space-filling technique to visualize the tag hierarchy and a circular layout with edge bundling to show the social network. It was tested on a Last.fm music community dataset and allowed tag-based and friend recommendation exploration.

1. SFViz
Interest-based Friends Exploration and
Recommendation in Social Networks
Liang Gou, Fang You, Jun Guo, Luqi Wu, Xiaolong (Luke) Zhang
College of Information Science & Technology
The Penn State University
School of Communication and Design
Sun Yat-Sen University

2. How to recommend friends by considering
both existing social connections of a person
and her dynamic interests?
Is there a visual exploration way
to dynamically recommend
friends by considering both
social connections and a context
of social connections (e.g.,
similar interest)?

3. Our Approach
• In this paper, we propose a novel visualization
tool to support users to explore and find friends
interactively with different interests.
– Social networks: measure how close people
are
– Social tags: measure the interest similarity

4. Overview
• Reviews relevant literature on tag visualization and social
recommendation visualization, profile-based and topology-
based recommendation approaches.
• Presents our framework and a hybrid approach of social
tags and social networks.
• Describes the visualization design and implementation of a
system, SFViz, to support user interaction and exploration.
• Illustrates a case study of using SFViz for friend exploration
and recommendation in a music community, Last.fm.

5. About SFViz- SFViz Framework

6. About SFViz- SFViz Framework
• Extract the information about user dynamic
interest based on tags created by users.
• Construct tag networks to reflect a user’s
interest and a hierarchical tag structure .
• Calculate similarities among people with the
information of tag networks and social
networks to recommend friends for users.

7. Generate a Matched Compound
Graph
• Matching Score: measures how appropriate an actor is
assigned to the smallest category (the deepest non-leaf
node) defined in the tag hierarchy.
Root 10 11
8 9
Depth=1
C1 C2 C3 7
Depth=2
C11 C12 C21 C22 C31 C32 C33 6 5
Depth=3 1 2 3 4
t1 t2 t3
ms(3,C21)=? { t1 t7 C21 C2 }

8. Calculate Actor Similarity

9. Visualization Design
To help users explore and interact recommended friends in
a compound graph, SFViz design support:
• Tag tree exploration and interaction, showing context
and details information (parent-child relation, siblings
nodes);
• Social network exploration and interaction, showing
highly connected cliques, direct friends, a critical path to a
friend and so on;
• Friend recommendation with context of interest, showing
potential friends with specified interests with different
granularities in a tag tree and how to reach these users.

10. Visualization Design
With SFViz framework, we need transform a matched
compound graph consisting of two sub-graphs of tree and
network, and social recommendation into visual forms.
To meet these requirements, we design and implement
SFViz with several key visualization techniques:
• Radial, Space-Filling (RSF) technique to visualize a
tag tree.
• Circle layout with edge bundling to show a social
network, highlighted social recommendation views and
several interactions.

11. Layout a Tag Tree with RSF
The tag tree is represented with a Radial, Space-Filling (RSF) technique
in RSFViz. The RSF uses nested circles to show the parent-child
relationship: the root node in the centre of a circle and child nodes
placed within the arc subtended by their parents.
• Tag nodes from a depth are assigned
along a circle with color showing their
depth. The tree hierarchy information is
shown with inclusion relationship in the
representation.
• Node width in a circle is proportional to
the count of all its children and leaf nodes
have a uniform size.
• Node width can be adjusted to show
more or less details of this node and its
descendants.

12. Circularly Layout Social Network
• Nodes in a social network are also the
leaf nodes in the tag tree in a matched
compound graph. To reflect the matched
relationship, we use a RSF tree as the
supporting structure and layout a social
network over the RSF tree.
The idea is to circularly arrange the
network nodes to corresponding
positions in the circle outlined by the
RSF tree, and then connects the node
sectors within the circle. This design
integrates both network and tree
structures in a single graph without
introducing extra nodes and links.

13. Case study
• Dataset: The dataset in our case study is from a social
music service Last.fm retrieved by Multimedia
Information Retrieval Group at Glasgow University in
November 2008.
• Data Preprocessing
• Tag-Based Multiscale and Cross-scale Exploration.
• Friend Recommendation Exploration.

14. Case study (Cont.)
Tag network of LastFm data after filtering.
Tag (category) tree with RSF representation.

15. Friend Recommendation
Exploration
A view of share friends without aggregation.
A view of share friends with aggregation.

16. This is an on going work
• We will extend the work in two directions. First, we will
conduct more experiments and user studies of our
approach. The experiments will assess the accuracy our
recommendation with some labelled dataset, and in user
studies, we may ask real users to rate friends
recommendation.
• Second, we will incorporate other methods and
information to classify users to a tag hierarchy, such as
user’s profile information.

17. Can I take questions?

18. Thank you for your attention!

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