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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.
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 C 21 C 22 C 31 C 32 C 33 6 5
Depth=3 1 2 3 4
t1 t2 t3
ms(3,C21)=? { t1 t7 C21 C2 }
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.
19. Reference
• Gou, L., Zhang, S. K., Wang, J. & Zhang, X. L. (2010). TagNetLens: Visualizing knowledge structures
with social tags. In Proc. of ACM VINCI’10, 18: 1-9.
• Marsh, D.R., Schroeder, D.G., Dearden, K.A., Sternin, J. & Sternin, M.(2004). The power of positive
deviance. British Medical Journal, 329, pp. 1177–1179.
• Israel, B.A. (1982). Social networks and health status: Linking theory, research and practice. Patient
Counseling and Health Education, 4(2), pp. 65-77.
• Davis, H, Vetere, F, Ashkanasy, et al. (2008) Towards Social Connection for Young People With Cancer.
OzCHI, Queensland.
• Goswami, S., Köbler, F., Leimeister, J. M. & Krcmar, H. (2010). Using online social networking to
enhance social connectedness and social support for the elderly. In Proc. of ICIS’10, pp. 109-120.
• Lampe, C., Ellison, N. & Steinfield, C. (2007). A familiar Face(book): Profile elements as signals in an
online social network. In Proc. of CHI’07, pp. 435-444.
• Krulwich, B. (1997). Lifestyle finder: intelligent user profiling using large-scale demographic data. Artificial
Intelligence Magazine, 18(2), pp. 37–45.
• Mooney, R. J. & Roy, L. (2000). Content-based book recommending using learning for text categorization.
In Proc. of DL’00, pp. 195-204.
Editor's Notes
Friend recommendation is popular in social network services to help people make new friends and expand their networks. Friend recommendation is either based on topological structures of a social network, or derived from profile information of users. However, dynamically recommending friends by considering both social connections and a context of social connections (e.g., similar interest) in a way of visual exploration is not well supported by existing tools.
How to recommend friends by considering both existing social connections of a person and her dynamic interests? In this paper, we propose a novel approach to support users to explore and find friends interactively with different interests
This paper is organized as the following. Section 2 reviews relevant literature on tag visualization and social recommendation visualization, profile-based and topology-based recommendation approaches. Section 3 presents our framework and a hybrid approach of social tags and social networks. Section 4 describes the visualization design and implementation of a system, SFViz, to support user interaction and exploration. Section 5 illustrates a case study of using SFViz for friend exploration and recommendation in a music community, Last.fm. The paper concludes with the discussion on the findings and implications of our research, and future research directions.
We design a visualization system, SFViz (Social Friends Visualization), to help users explore recommendation with different interests in an interactively way. SFViz framework is shown in Figure 1. This framework follows the idea of the reference model by Card et al. [38]. On the top, the data model takes social networks and social tags as input and then data model is transformed into visual forms to users for exploration and interaction. Users can manipulate both data model and visual forms on their demand. In data model, social tags are converted to a tag network (Section 3.1) and then a tag hierarchy is created with the tag network (Section 3.2). People in social networks are matched to the tag hierarchy. This results in a compound graph including both tree and network structure (Section 3.4). Then, actor similarity under a specified context of interest is calculated to recommend friends to users (Section 3.5
More specifically, we first extract the information about user dynamic interest based on tags created by users in a social network service system. We construct tag networks to reflect a user’s interest and a hierarchical tag structure implying a knowledge structure shared among those people who generated them. Then, we calculate similarities among people with the information of tag networks and social networks to recommend friends for users.
𝐴𝑆 (𝑖,𝑗)=cos(𝑖,𝑗)=(𝑣_𝑖⋅𝑣_𝑗)⁄(|𝑣_𝑖 |⋅|𝑣_𝑗 |)
With SFViz framework shown in Figure 1 (Section 3.1), 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: a Radial, Space-Filling (RSF) technique to visualize a tag tree, a circle layout with edge bundling to show a social network, highlighted social recommendation views and several interactions.
With SFViz framework shown in Figure 1 (Section 3.1), 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: a Radial, Space-Filling (RSF) technique to visualize a tag tree, a circle layout with edge bundling to show a social network, highlighted social recommendation views and several interactions.
In the part of visual form, a compound graph is visually represented as an integration of RSF tree and circular network (Section 4). Various tools are designed to support user interaction with the compound graph. Recommended friends are visually encoded to the compound graph with similarity scores. Users can change a tag of interest to update recommended friends with the context information. 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. The RSF technique clearly illustrates the parent-child relationship in the tree and also node area to present node properties [43][44]. Figure 6a is an example of the RSF visualization of a tag tree structure shown in Figure 4a. The root node is placed in the center and shown in transparent. 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. We can also see that 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.
Two examples of circular layout of social networks in SFViz design are shown in Figure 6b and 6c.In Figure 6b, the social network in Figure 4a is circularly placed along in a RSF tree. The aggregated social network of Figure 5 is also shown in Figure 6c. We show expanded parent tags as the context of child tags which are transparent and labelled with grey color. We use the color of tag with a higher level to encode an aggregated edge which collects tags from two different levels. For example, has the same the color of C 1 is used for the edge between node 6 and C 1 because C 1 has a higher scale than node 6. The aggregated weight of an edge is shown with thickness. Both Figure 6b and 6c uses straight lines to show edges in circular layout, which results in a problem that some edges may be occluded by node sectors. For example, the node sector C 1 interrupts the edge between node 6 and C 1 . To alleviate this issue, we introduce a technique of edge bundling in the following section.
Illustrates a case study of using SFViz for friend exploration and recommendation in a music community, Last.fm.
SFViz can suggest friends to a user based on the user’s social network information and a given interest category in a tag tree. After the user is known (e.g., with log-on information or by specifying from a network), the Top-N recommended people are shown with gradient colors. Figure 18 shows the top 10 recommended friends for the center user who is purple at the left bottom. The recommended people are shown with colors from red to yellow based on their rankings. In this example, no category of interest is specified. When a category of interest is specified in the tag tree, social recommendation will be adjusted dynamically. Figure 19 shows the 10 recommended friends with a tag category of “hip hop” for the same user in Figure 18. We can see that the recommended friends are narrowed down to the category of “hip hop”.
