Romantic Partnerships and the Dispersion of Social Ties Lars - PowerPoint PPT Presentation

Introduction Embeddedness and Dispersion Evaluation Combining Features Romantic Partnerships and the Dispersion of Social Ties Lars Backstrom Jon Kleinberg presented by Yehonatan Cohen 2014-11-12 Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features 1 Introduction Problem Statement Dataset 2 Embeddedness and Dispersion Embeddedness Dispersion 3 Evaluation Take 1 Take 2 Time and Space 4 Combining Features Machine Learning Performance Over Time Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Problem Statement Dataset Problem Statement Consider a social network user, a Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Problem Statement Dataset Problem Statement Consider a social network user, a , and its neighborhood... Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Problem Statement Dataset Problem Statement Consider a social network user, a , and its neighborhood... Also, let us assume that a is married. Can we identify his wife? Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Problem Statement Dataset Problem Statement Formally, our problem is defined as follows: Spouse Detection Let a an ego Facebook node and denote by G a its set of all friends and the links among them. Given a declared a relationship partner (’married’, ’engaged’ or ’in a relationship’). Can we identify a ’s spouse? Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Problem Statement Dataset Motivation Such relationships detection is important for several reasons: Romantic relationships are singular type of social ties that play powerful roles in social processes over a person’s whole life course. They also form an important aspect of the everyday practices and uses of social media. They are among the very strongest ties, but is has not been clear whether standard structural features are sufficient to characterize them. Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Problem Statement Dataset Facebook Semantcis Facebook is the most popular on-line social network. Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Problem Statement Dataset Facebook Semantcis Facebook is the most popular on-line social network. A user is represented by a node. Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Problem Statement Dataset Facebook Semantcis Facebook is the most popular on-line social network. A user is represented by a node. Facebook’s friendship relation is undirected. Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Problem Statement Dataset Facebook Semantcis Facebook is the most popular on-line social network. A user is represented by a node. Facebook’s friendship relation is undirected. An edge between two nodes represents a friendship between the corresponding users. Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Problem Statement Dataset Facebook Semantics Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Problem Statement Dataset Datasets Description Two datasets were used by the authors: The first consists of the network neighborhoods of approximately 1.3 million Facebook users. Users were selected uniformly at random from among: Users of age at least 20. Users with between 50 and 2000 friends. Users who list a spouse or relationship partner in their profile. Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Problem Statement Dataset Datasets Description The second is a sample of approximately 73,000 neighborhoods from the first dataset selected uniformly at random from among all neighborhoods with at most 25,000 links. Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Problem Statement Dataset Datasets Dimensions The datasets contains 379 million nodes. Overall there are 8.6 billion links. An average of 291 nodes and 6652 links per node’s neighborhood. Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Problem Statement Dataset 1 Introduction Problem Statement Dataset 2 Embeddedness and Dispersion Embeddedness Dispersion 3 Evaluation Take 1 Take 2 Time and Space 4 Combining Features Machine Learning Performance Over Time Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Embeddedness Dispersion Embeddedness Embeddedness Given an edge ( u , v ), its embeddedness is the number of mutual friends shared by its endpoints. Traditionally, embeddedness is associated with tie strength, and will be used as a baseline predictor. Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Embeddedness Dispersion Embeddedness What is the embeddedness of ( b , c )? Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Embeddedness Dispersion Embeddedness What is the embeddedness of ( b , c )? Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Embeddedness Dispersion Embeddedness Can you determine the strongest tie in the network below? Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Embeddedness Dispersion Embeddedness Can you determine the strongest tie in the network below? Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Embeddedness Dispersion Dispersion Many individuals have large clusters of friends corresponding to well-defined foci of interaction in their lives: Co-workers. People with whom they attended college. Family members. Etc. Since many people within these clusters know each other, the clusters contain links of very high embeddedness even though they do not necessarily correspond to particularly strong ties. Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Embeddedness Dispersion Dispersion In contrast, the links to a person’s relationship partner may have lower embeddedness, but they will often involve mutual neighbors from several different foci, reflecting the fact that the social orbits of these friends are not bounded within any one focus. Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Embeddedness Dispersion Dispersion Consider the following example: A husband who knows several of his wife’s co-workers, family members, and former classmates, even though these people belong to different foci and do not know each other. Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Embeddedness Dispersion Dispersion The mutual neighbors of a married couple are not well-connected to one another. Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties

Introduction Embeddedness and Dispersion Evaluation Combining Features Embeddedness Dispersion Dispersion Dispersion We take the subgraph G u induced on u and all neighbors of u , and for a node v in G u we define C uv to be the set of common neighbors of u and v . Then disp ( u , v ) = Σ s , t ∈ C uv d v ( s , t ) Lars Backstrom, Jon Kleinberg Romantic Partnerships and the Dispersion of Social Ties