In social networks, where users send messages to each other, the issue of what triggers communication between unrelated users arises: does communication between previously unrelated users depend on friend-of-a-friend type of relationships, common interests, or other factors? In this work, we study the problem of predicting directed communication intention between two users. Link prediction is similar to communication intention in that it uses network structure for prediction. However, these two problems exhibit fundamental differences that originate from their focus. Link prediction uses evidence to predict network structure evolution, whereas our focal point is directed communication initiation between users who are previously not structurally connected. To address this problem, we employ topological evidence in conjunction to transactional information in order to predict communication intention. It is not intuitive whether methods that work well for link prediction would work well in this case. In fact, we show in this work that network or content evidence, when considered separately, are not sufficiently accurate predictors. Our novel approach, which jointly considers local structural properties of users in a social network, in conjunction with their generated content, captures numerous interactions, direct and indirect, social and contextual, which have up to date been considered independently. We performed an empirical study to evaluate our method using an extracted network of directed @-messages sent between users of a corporate microblogging service, which resembles Twitter. We find that our method outperforms state of the art techniques for link prediction. Our findings have implications for a wide range of social web applications, such as contextual expert recommendation for Q&A, new friendship relationships creation, and targeted content delivery.

1. Predicting Communication Intention
in (Enterprise) Social Networks
Charalampos “Harris” Chelmis
Computer Science, University of Southern California
Thanks to: Viktor K. Prasanna, Ming Hsieh Department of Electrical Engineering, USC
Vikram Sorathia, Co-founder & CEO at Kensemble Tech Labs LLP
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2. Social Networks are Everywhere
2
• , ,
• Movie Networks
• Affiliation/co-authorship networks
• Professional networks
• Friendship networks
• Information networks
• Organizational Networks
• Q&A websites
• Even networks

3. • Multiple applications
 Targeted marketing
 Personalization
− Content delivery
 Recommendation
− People to connect, items to buy, movies to watch
 Law enforcement
− Fraud detection
− Guilt by association
 Epidemiology
 Information dissemination/propagation
 …
• Users interact with one another and content they create and
consume
 Rich interactions
− Friendships based on similarity
− Following based on interest
 Noisy
Social Network Analysis
3

4. • Collaboration Enabling Technologies
 Multiple communication channels
 Spread of timely and relevant information
 Search for data and experts
Collaboration Technologies at the Workplace
4

5. • Main focus on business perspective
 Less noisy than online social networks
 Q&A
 Problem solving
 Information seeking
• But also
 Assist in breaking barriers
 Team building
 Knowledge propagation
• Opportunities
 Expert identification
− Experts vs. Influencers
 Information Flow
 Trends
− Technology adoption
− Company focus
Collaboration at the Workplace
5

6. • More Opportunities
 Collective Knowledge
− Generation
− Sharing
 Collaborative Knowledge Management
− How do employees work together to complete tasks?
− How does innovation happen?
− Best practices
• Difficulties
 Informal interactions
 Heterogeneous, unstructured data
 How to formally model knowledge?
Collaboration at the Workplace
6

7. • Descriptive Modeling
 Social network analysis
• Predictive modeling
 Link prediction
 Attribute prediction
• Typically networked data are represented as graphs
 Nodes (e.g., users)
 Edges
− Social relations
− Interactions
− Information flow
− Similarity
 Weight
− Communication frequency
− Communication cost (e.g., distance)
− Reciprocity
− Type of interaction (e.g., family member, friend, or officemate)
Networked Data Modeling
7

8. • Heterogeneous object and link types
• Both nodes and edges may carry attributes
• Attribute dependencies
 Correlation between attribute values and link structure
− e.g. link prediction based on auxiliary information
 Correlation among attributes of related nodes
− e.g. collaborative filtering
• Node dependencies
 e.g. groups/communities
• Partial observations
 e.g. labels
But Networked Data are Very Different than Graphs
8

9. • Big Data
 Billions of users
 Billions of connections
 Billions of “documents”
• Temporality
 Affiliations
 Interests
 Friendships
• Context
 Spatial
 Temporal
 Topical
• Content multimodality
 Text
 Multimedia
Networked Data ≠ Graphs
9

10. • Edges are more than links
 Type
− e.g. like vs. comment vs. share
 Trust
 Sentiment
 Strength
 Time
 Number
• Edges “reveal” something about the relation between nodes
 Prior “interaction” to compute similarity
Networked Data ≠ Graphs
10

11. Networked Data ≠ Graphs
11

12. • Integrated informal communication
• Context sensitive
• Temporal
• External Sources
• Analysis of implicit relations
Holistic Modeling of Complex Networks
12
Multiple collaborative
platforms
Multimodal,
heterogeneous content
from various sources
Meta-information
about content
- Social Algebraic Operations
- Complex mining and analysis
- Correlation of different domains
- Temporal, semantic analysis
context
time
content
connection

13. • Directed communication graph G = (V,E)
 Node u represents a user
 Edge e = (u,v) exists iff user u has sent at least one message to user v
• Input
 G0 = (V0,E0), subgraph of G consisting of all nodes in G and a subset of
edges in G
• Output
 Ranked list L of edges, not present in G0, such that
Predicting Intention of Communication
13
ELE 0
OutputInput
u
G0
u
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14. • Edge semantics:
 Conversation between users rather than friendship
•
•
• “What makes people initiate conversations with strangers?”
• “With whom do individuals choose to collaborate and why?”
≠ Link Prediction
14
Contextual – Temporal Properties
Directionality Matters
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15. • The tendency to relate to people with similar characteristics
 status, beliefs, etc.
• Fundamental concept underlying social theories (e.g. Blau 1977)
• Fundamental basis for links in many types of social networks
 “Similar” nodes tend to cluster together
• How does this helps us solve our problem?
Homophily
15

16. • Machine learning
 Probabilistic, supervised, computationally expensive
• Node attributes
 No semantics
 We instead exploit multiple features of variable types
• Network structure
How to Compute Similarity?
16
Graph Distance Length of shortest path between u and v
Common Neighbors
Jaccard Coefficient
Adamic/Adar
Preferential Attachment
Katz
Random walks
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17. • If there is a tie between x and y and one between y and z, then in
a transitive network x and z will also be connected
• Such structural clues have been traditionally used for link
prediction
• Consider what happens if edge semantics change
• Or if we further include context
Transitivity
17
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z
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asks ?

18. Communication Network
18
Threaded
Discussion
Bipartite
Graph
Post-Reply
Network

19. Augmented, Directed Post-Reply Network
19

20. • We model a user as a union of her:
 connections and
 her content
• We characterize microblogs using a set of attributes
 each feature according to its type
 Textual Features
− raw textual content (bag-of-words)
− #hashtags
− Groups
 Temporal Features
− Date
− Time
• WordNet: enrich concepts with conceptually, semantically and
lexically related terms
 Synonyms
 Hypernyms
 Hyponyms
User Representation
20

21. • Semantic Similarity of textual concepts
 Jaccard Index:
 Synonym-based similarity:
 Hypernym-based similarity:
 Hyponym-based similarity:
• Calculate Semantic Similarity using weighted sum
Semantic Similarity of Textual Features
21
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22. • Caveat: concepts belong to the same subtree
 Solution: compute similarity between the union of annotations
• Account for lexical similarity: Levenshtein similarity
• Select the highest similarity, either semantic or lexical
Semantic Similarity of Textual Features
22
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23. • Textual Similarity between bag-of-words features:
 tf.idf weight vector representation
 Cosine similarity
• Date Similarity:
• Time Similarity:
• Timestamp similarity:
Feature Similarity
23
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24. • We use a variation of Hausdorff point set distance measure:
 Average of the maximum similarity of features in set A with respect to
features in set B

 : any similarity measure on set elements ak and bi
 Measure is asymmetric with respect to the sets
Feature Set Similarity
24
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25. • A weighted function of content and network proximity
 λ controls the tradeoff between content and network proximity
• Content Proximity
 User similarity with respect to their microblogs
 Similarity of microblogs
− Combined weighted value of respective attributes similarities
• Network Proximity:
User Similarity
25
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26. • First construct the augmented communication graph G(V,E)
• Given a user u,
 compute users similarity
− For all posts of user u with respect to all other users in the network
 For all facets
Communication Intention Prediction
26

27. • Complete snapshot (June 2010 – August 2011) of a corporate micro-
blogging service, which resembles Twitter
 4,213 unique users
 16,438 messages in total
− 8,174 thread starters
− 8,264 replies
 8,139 threads
 88 discussion groups
 637 unique #hastags
Dataset
27

28. • In our evaluation we focus on the Largest Connected Component
 582 users
 3,773 directed edges
 11,684 messages
 Average degree = 12.97
• Clustering coefficient = 0.2311 >> ccrandom = 0.0223
• Clustering coefficient as a function of node degree
 Average clustering coefficient decreases with increasing node degree
 Higher for nodes of low degree significant clustering among low-
degree nodes
Dataset
28


29. Number of Neighbors
• Directed messages received vs. directed messages sent
 Scattered across the diagonal
 Cumulative distribution of the out-degree to in-degree ratio, exhibits
high correlation between in-degree and out-degree
 Tendency of users to reply back when they receive a message from
other users?
29

30. • Four-fold cross validation
• Randomly sample 100 users & recommend top-k links for each user
• Accuracy measures
 Precision@k
 Recall@k
 MRR
• Baselines
 Random
− Random selection
 Shared Vocabulary
− Cosine similarity based on #hastags vector
 Shortest distance
− Length of the shortest path
 Common neighbors
−
Evaluation
30
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31. Lexical and Topical Alignment
• Is there a global vocabulary in the corporate microblogging service?
 Hashtags vocabulary
 “Groups vocabulary”
• Select user pairs at random and measure number of shared tags
 Average nst = 1.001
 Most common case is the absence of shared tags
• However adjacent users in social networks tend to share common
interests due to homophily
 We measure user homophily with respect to hashtags as a function of
the distance of users in the network
• Select user pairs at random and measure number of shared groups
 Average nsg = 1
 Most common case is the absence of shared groups
31

32. Lexical Alignment
• Average number of shared (distinct) hashtags for two users as a
function of their distance d along the network:
,
• Shared hashtags vocabulary up to distance 6!
32
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33. • Bold indicates best performing baseline
• Percentage lift
 the % improvement achieved over the best performing baseline
Methods Comparison
33

34. • How to choose best values of λ and weighing factors?
• Different datasets may lead to different optimal values
 Grid search over ranges of values for these parameters
 Measure accuracy on the validation set for each configuration setting
Weight Scheme Selection
34

35. • 0 only considers network proximity
• 1 only considers content similarity
• All schemes perform better than the baseline
• Good value for λ is approximately 0.8
Effect of Parameter λ
35

36. • Effect of weighting schemes on accuracy per user
• Different weighting schemes perform better for different users
 Features importance is user specific
• Need personalization to achieve better accuracy overall
Effect of Weighting Scheme
36

37. • Average precision (measured@ 5) of users having k
 (a) posts or
 (b) neighbors in the communication network
 The more statistical evidence the better the overall precision
Content Availability and Structural Proximity
37

38. • MRR as a function of λ for various restrictions
• Greater statistical evidence results in more accurate predictions
Content Availability and Structural Proximity
38

39. • Performed modeling and analysis of informal communication at the
workplace
• We introduced the problem of communication intention prediction
• We addressed this problem by exploiting auxiliary information
 Holistic modeling of structural clues and semantically enriched
content
• We tested the efficiency of our approach in a real-world dataset
 The more statistical evidence available, the more accurate predictions
 Need for personalization
• Potential applications
 Contextual expert recommendation for Q&A
 Search for “interesting” people to collaborate
• Open problems
 Scalability
 Replication of results for online social media
Conclusion and Open Problems
39

40. • Semantic Social Network Analysis for the Enterprise
Contextual Recommendation
40
Employee ID:

41. • Semantic Social Network Analysis for the Enterprise
 Instantiate our modeling in Ontology
 Collaboration analytics at the workplace
 Real-world data evaluation
Contextual Recommendation
41
Contextual ego-
network analysis
Expert
Identification
Semantic Analysis

42. • Questions?
• Resources
 http://www-scf.usc.edu/~chelmis/index.php
 http://pgroup.usc.edu/wiki/CSS
• Please send all inquiries at chelmis@usc.edu
Thank you!
42