Simulation and Analysis of Question Routing in Social Networks Ganesh Bhat & Swathi Bhat

Introduction Question Routing as an alternative to the Q/A approach Compare three approaches to routing questions in this network: Random Rank Friend Rank Expert Rank Modeled a virtual social network composed of People who can be friends with other people in the network and who have a knowledge score.

Topics of Discussion Modeling Framework Modeling the Social Network Modeling Knowledge Modeling Question Generation Routing Approaches Routing Metrics Statistical Analysis of the Routing Approaches Next Steps

The idea: In this simulation, we compare three approaches to routing questions through a social network, evaluating each approach in terms of the extent to which these questions are answered. By evaluating these, we seek to explore the routing approaches for question/answer systems on social networks. 

Modeling the Framework SOCIAL NETWORK Social Network

Modeling the Framework SOCIAL NETWORK Social Network Person

Modeling the Framework SOCIAL NETWORK Social Network Person FriendMap

Modeling Framework Tag..n Tag 3 Tag 1 Tag 2 Social Network Knowledge Person FriendMap Knowledge Tag Question Answer QuestionMap Tag 3 Tag 1 Tag 2

Modeling the Framework SOCIAL NETWORK Social Network Person FriendMap Knowledge Tag Question Answer QuestionMap Simulator Random Rank RANDOM RANK: P randomly selects a Friend F on the network and routes the question to him

Modeling the Framework SOCIAL NETWORK Social Network Person FriendMap Knowledge Tag Question Answer QuestionM ap Simulator Random Rank Friend Rank FRIEND RANK: P passes question Q to the most knowledgeable friend F.

Modeling the Framework SOCIAL NETWORK Social Network Person FriendMap Knowledge Tag Question Answer QuestionMap Simulator Random Rank Friend Rank Expert Rank EXPERT RANK: P passes question Q to friend F with highest ExpertRank for Q.

Modeling the Social Network Our virtual social network is composed of individuals who can be friends with other Persons on the network. Friendships are randomly distributed across the virtual social network. Knowledge is an entity associated with each person in the network.

Modeling Knowledge Knowledge is modeled through the use of tags. A finite dictionary of tags represent all topics of knowledge. Knowledge scores are distributed in a random fashion across the network and across all the tags. Knowledge score varies between 0 and 1 for each and every tag in our finite dictionary of tags. Knowledge scores do not vary if the user chooses not to respond to a question posed to him.

Modeling Questions Questions are modeled to consist of tags. At the beginning of our simulation we randomly generate a set of questions. Questions are generated in an incremental fashion. To generate a question we randomly select a set of tags.

Modeling Response Set the state of the question Q to active. Initially each author of the question has a copy of the question. While there exists an Active question in the network Select an active question Q in a random fashion. Select a random person P to be holding the question Q. [P makes a decision as to whether to respond to the question at all] If P decides to respond to the question. [P can decide to answer Q or pass Q to a friend F If P decides to answer Q. [ P provides a random answer A for Q] If P decides to Pass Q to a friend [P uses one of the routing approaches] Question marked inactive for P.

Modeling Response: Assumptions P can answer or route a question and not both. P can answer or route a question just once. Questions can be routed only to friends. FriendResponseRate: When deciding to answer or route a question that is authored by a friend, all friends have the same probability. NonFriendResponseRate: When deciding to answer or route a question authored by a non-friend, all non- friends have the same probablility. P will answer a question if they have a certain minumum knowledge to answer.

Routing Approaches Random Rank: RandomRank is the baseline for comparing the various approaches.  With this approach, person P randomly selects one of her friends and routes the question to that friend.

Routing Approaches FriendRank FriendRank extends RandomRank, but passes the question to the most knowledgeable friend.  FriendRank assumes that every friend knows how knowledgeable his or her friends are, and it passes the question to the most knowledgeable friend. .FriendRank only maintains the local perspective of the sender.  In particular, the sender has no information about his friends' friends knowledge.

Routing Approaches Expert Rank Expert Rank further revises FriendRank and routes the question to the friend with highest centrality rating. Therefore Friends’ friends expertise is also considered. Our algorithm takes into account the popular Page Rank algorithm. Centrality scores are assigned to each person in a question map of Q. A question map of Q, M(Q) is a directed graph of persons who have sent or received a question.

Routing Approach contd.., Expert Rank…. Each Question Map is a tree rooted at the author of the question. Each person has a single global expert rank that is calculated from the sum of the scores of P on all the tags that constitute the Question. A persons’ expertise at a tag is calculated by taking the union of all question maps whose question contains tag T.

Evaluation Metrics Answer Ratio = (the number of people who answered the question) / (the # of people who received the question) Pass Ratio = (the number of people who passed the question to another person) / (the # of people who received the question) Average Knowledge Per Question = (for each question answered, sum of the knowledge scores of all the people who answered the question) / (the # of questions)

Metrics used for simulation # of Persons = size of social network # of Friendships Per Person. # of Tags = size of the tag dictionary # of Questions = the number of questions asked # of Tags Per Question > Friend Response Rate = the probability that a person will respond to a friends question by either answering the question or passing it to another friend. > Non-friend Response Rate = the probability that a person will respond to a non-friends question by either answering the question or passing it to another friend. > Maximum # of Passes Per Question > RouterApproachType = algorithm which decides which friend to pass a question to = (RandomRank, FriendRank, ExpertRank)

Results:

Conclusions We successfully designed and implemented an abstract framework for testing question routing. We are using this framework to compare the performance of three approaches to routing questions, RandomRank, FriendRank, and ExpertRank.  We believe that we may be able discern significant performance differences by tweaking the indepedent variables, and/or changing the initial topology of the question maps. 

Next Steps: Revise ExpertRank, which currently only uses centrality to discern expertise. Accuracy, timeliness… Revise and test certain assumptions, which we think would make the question routing model more realistic.   Test our model on actual distributions of tags based on the frequencies of words in the English language.

Acknowledgement Prof Ling Liu