Scalable Learning of Collective Behavior Based on Sparse Social Dimensions

Lei Tang, Huan LiuCIKM’09

Speaker: Hsin-Lan, WangDate: 2010/02/01

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Outline Introduction Collective Behavior Learning Social Dimensions Algorithm

Edge-Centric View K-means Variant

Experiment Setup Experiment Results Conclusions and Future Work

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Introduction

Social media facilitate people of all walks of life to connect to each other.

We study how networks in social media can help predict some sorts of human behavior and individual preference.

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Introduction

In social media, the connections of the same network are not homogeneous. However, this relation type information is not readily available in reality.

A framework based on social dimensions is proposed to address this heterogeneity.

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Introduction In the initial study, modularity

maximization is exploited to extract social dimensions.

With huge number of actors, the dimensions cannot even be held in memory.

In this work, we propose an effective edge-centric approach to extract sparse social dimensions.

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Collective Behavior Learning

When people are exposed in a social network environment, their behaviors can be influenced by the behaviors of their friends.

People are more likely to connect to others sharing certain similarity with them.

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Collective Behavior Learning K class labels network

V is the vertex set, E is the edge set and are the class labels of a vertex

Given known values of for some subsets of vertices .

How to infer the values of for the remaining vertices

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Social Dimensions

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Social Dimensions To address the heterogeneity present

ed in connections, we have proposed a framework (SocDim) for collective behavior learning.

Framework SocDim is composed of two steps:1. social dimension extraction2. discriminative learning

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Social Dimensions

These social dimensions can be treated as features of actors.

Since network is converted into features, typical classifier such as support vector machine can be employed.

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Social Dimensions Concerns about the scalability of SocDim wi

th modularity maximization: The social dimensions extracted according to m

odularity maximization are dense. Requires the computation of the top eigenvecto

rs of a modularity matrix which is of size n*n. The dynamic nature of networks entails efficient

update of the model for collective behavior prediction.

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Algorithm -Edge-Centric View

Treat each edge as one instance, and the nodes that define edges as features.

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Algorithm -Edge-Centric View

Based on the features of each edge, we can cluster the edges into two sets.

One actor is considered associated with one affiliation as long as any of his connections is assigned to that affiliation.

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Algorithm -Edge-Centric View In summary, to extract social

dimensions, we cluster edges rather than nodes in a network into disjoint sets.

Because the affiliations of one actor are no more than the connections he has, the social dimensions based on edge-centric clustering are guaranteed to be sparse.

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Algorithm -K-means Variant

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Algorithm

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Experiment Setup -Social Media Data

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Experiment Results -Prediction Performance

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Experiment Results -Prediction Performance

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Experiment Results -Prediction Performance

Prediction performance on all the studied social media data is around 20-30% for F1 measure. This is partly due to : large number of labels in the data only employ the network information

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Experiment Results -Scalability Study

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Experiment Results -Scalability Study

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Experiment Results -Sensitivity Study

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Conclusions and Future Work To address the scalability issue, we

propose an edge-centric clustering scheme to extract social dimensions and a scalable k-means variant to handle edge clustering.

The model based on the sparse social dimensions shows comparable prediction performance as earlier proposed approaches to extract social dimensions.

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Conclusions and Future Work

In reality, each edge can be associated with multiple affiliations while our current model assumes only one dominant affiliation.

The proposed EdgeCluster model is sensitive to the number of social dimensions.