Social Network Analysis 10 Most Popular Websites Site

Social Network Analysis

10 Most Popular Websites Site Alexa traffic rank (May 2013) Domain Google Display Network Ad Planner (July 2011) Linking root domains (May 2013) Type Alexa traffic rank Facebook facebook. com 1 8, 190, 877 1 Social Networking Google google. com 2 4, 533, 883 NA Search You. Tube youtube. com 3 3, 637, 788 2 Video-Sharing Yahoo! yahoo. com 4 1, 888, 093 3 Search Baidu baidu. com 5 325, 710 8 Search Wikipedia wikipedia. org 6 2, 154, 423 6 Reference Windows Live live. com 7 149, 315 4 Portal Amazon. com amazon. com 8 1, 177, 136 24 Commerce Tencent QQ qq. com 9 472, 087 10 Instant Messaging 15 Microblogging / Instant Messaging / Social Media Twitter twitter. com 10 6, 183, 107 Ranking measures Alexa Internet ranks websites based on a combined measure of page views and unique site users. Alexa creates a list of "top websites" based on this data timeaveraged over three month periods. Linking root domains The number of linking root domains is a measure of how many external sites link to the website. Google Display Network Ad Planner The Google Display Network Ad Planner measures the number of unique visitors, for use by Google's advertisers.

SOCIAL NETWORK = SOCIA MEDIA + NETWORKING

SOCIAL MEDIA IS AN UMBRELLA TERM THAT DEFINES THE VARIOUS ACTIVITIES THAT INTEGRATE TECHNOLOGY, SOCIAL INTERACTION, AND THE CONSTRUCTION OF WORDS, PICTURES, VIDEOS AND AUDIO. http: //www. wikipedia. org

More simply put: “Social media is people having conversation online. ”

The conversations are powered by … • Blogs • Micro Blogs • Online Chat • RSS • Video Sharing Sites • Photo Sharing Sites …

“WHY SHOULD I CARE? ”

Reason #1 SOCIAL-NETWORKING SITES ARE THE MOST POPULAR SITES.

BECAUSE 3 OUT OF 4 AMERICANS USE SOCIAL TECHNOLOGY Forrester, The Growth of Social Technology Adoption, 2008

BECAUSE 2/3 of THE GLOBAL INTERNET POPULATION VISIT SOCIAL NETWORKS Nielsen, Global Faces & Networked Places, 2009

Reason #2 78% OF PEOPLE TRUST THE RECOMMENDATIONS OF OTHER CONSUMERS. NIELSEN “TRUST IN ADVERTISING” REPORT, OCTOBER 2007

Reason #3 BECAUSE TIME SPENT ON SOCIAL NETWORKS IS GROWING AT 3 X THE OVERALL INTERNET RATE, ACCOUNTING FOR ~10% OF ALL INTERNET TIME. Nielsen, Global & Networked Places, 2009

What is a Social Network ? • Network – a set of nodes, points or locations connected

What is a Social Network ? • Social Network - a social structure made up of individuals (or organizations) called "nodes", which are tied (connected) by one or more specific types of interdependency, such as friendship, common interest

What is a Social Network ? • Social Network Analysis (SNA) - views social relationships in terms of network theory consisting of nodes and ties (also called edges, links or connections).

Flickr – Social Engagements

Flickr users who commented on Marc_Smith’s photos (more than 4 times)

Human Super-Connectors Flickr users who commented on Marc_Smith’s photos (more than 4 times)

Flickr – Network Analysis

Friendship Network

Scientific collaboration network

Business ties in US biotech-industry

Genetic interaction network

Protein-Protein Interaction Networks

Transportation Networks

Internet

What is Network Analysis? • A set of relational methods for systematically understanding and identifying connections among actors • The advantage of social network analysis is that, unlike many other methods, it focuses on interaction (rather than on individual behavior). • Network analysis allows us to examine how the configuration of networks influences individuals and groups, organizations, and systems, and how these groups function. • It can be applied across disciplines—there are social networks, political networks, electrical networks, transportation networks, and so on.

What is Network Analysis? Network analysis assumes that: • How actors behave depends in large part on how they are linked together • Example: Adolescents with peers that smoke are more likely to smoke themselves. • The success or failure of organizations may depend on the pattern of relations within the organization • Example: The ability of companies to survive strikes depends on how product flows through factories… • Patterns of relations reflect the power structure of a given setting, and clustering may reflect coalitions within the group • Example: Overlapping voting patterns in a coalition government

Basic Concepts Network Components • Actors (nodes, points, vertices): - Individuals, Organizations, Events … • Relations (lines, arcs, edges, ties): between pairs of actors. - Undirected (symmetric) / Directed (asymmetric) - Binary / Valued Networks can be represented as • Adjacency Matrix • Adjacency List

Basic Concepts Types of network data: 1) Egocentered Networks • Data on a respondent (ego) and the people they are connected to. Measures: Size Types of relations

Basic Concepts Types of network data: 2) Complete Networks • Connections among all members of a population. • Data on all actors within a particular (relevant) boundary. • Never exactly complete (due to missing data), but boundaries are set • Ex: Friendships among workers in a company. Measures: Graph properties Density Sub-groups Positions

Measuring Networks: Connectivity Indirect connections are what make networks systems. One actor can reach another if there is a path in the graph connecting them. b a a d c b e f c f d e Basic elements: • Path: A sequence of nodes and edges starting with one node and ending with another, tracing the indirect connection between the two. On a path, you never go backwards or revisit the same node twice. Example: a b c d • Walk: is any sequence of nodes and edges, and may go backwards. Example: a b c b c d • Cycle: is a path that starts and ends with the same node. Example: a b c a

Measuring Networks: Distance & number of paths Distance is measured by the (weighted) number of relations separating a pair, Using the shortest path. Actor “a” is: 1 step from 4 2 steps from 5 3 steps from 4 4 steps from 3 5 steps from 1 a

Measuring Networks: Centrality refers to (one dimension of) location, identifying where an actor resides in a network. Centrality is fairly straight forward: we want to identify which nodes are in the ‘center’ of the network. In the sense that they have many and important connections. Following standard centrality measures capture a wide range of “importance” in a network: • Degree centrality • Closeness centrality • Betweenness centrality • Page. Rank

Measuring Networks: Centrality The most intuitive notion of centrality focuses on degree. Degree is the number of edges, and the actor with the most edges is the most important:

Measuring Networks: Centrality Degree Centrality: Relative measure of Degree Centrality:

Measuring Networks: Centrality A second measure is closeness centrality. An actor is considered important if he/she is relatively close to all other actors. Closeness is based on the inverse of the distance of each actor to every other actor in the network. Closeness Centrality: Relative Closeness Centrality

Measuring Networks: Centrality Relative Closeness Centrality

Measuring Networks: Centrality Betweenness Centrality: Model based on communication flow: A person who lies on communication paths can control communication flow, and is thus important. Betweenness centrality counts the number of shortest paths between i and k that actor j resides on. b a C d e f g h

Measuring Networks: Centrality Betweenness centrality can be defined in terms of probability (1/gij), CB(pk) = iij(pk) = = gij = number of geodesics that bond actors pi and pj. gij(pk)= number of geodesics which bond pi and pj and content pk. iij(pk) = probability that actor pk is in a geodesic randomly chosen among the ones which join pi and pj. Betweenness centrality is the sum of these probabilities (Freeman, 1979). Normalizad: C’B(pk) = CB(pk) / [(n-1)(n-2)/2]

Measuring Networks: Centrality Normalized Betweenness Centrality:

Measuring Networks: Centrality Betweenness Centrality:

Measuring Networks: Centrality Comparing across centrality values • Generally, the 3 centrality types will be positively correlated • When they are not correlated, it probably tells you something interesting about the network. Low Degree High Closeness Key player tied to important/active alters High Betweenness Ego's few ties are crucial for network flow Low Closeness Low Betweenness Embedded in cluster that is far from the rest of the network Ego's connections are redundant communication bypasses him/her Probably multiple paths in the network, ego is near many people, but so are many others Very rare cell. Would mean that ego monopolizes the ties from a small number of people to many others.

Measuring Networks: Centralization If we want to measure the degree to which the graph as a whole is centralized, we look at the dispersion of centrality: Freeman’s general formula for centralization (which ranges from 0 to 1):

Measuring Networks: Centralization Degree Centralization Scores Freeman: 1. 0 Freeman: . 02 Freeman: 0. 0

Measuring Networks: Density is defined as the number of connections an actor has, divided by the total possible connections an actor could have. The more actors are connected to one another, the more dense the network will be. Undirected network: n(n-1)/2 possible pairs of actors. Δ= Directed network: n(n-1)*2/2 possible lines. ΔD =

Measuring Networks: Density Freeman: . 25 Freeman: . 23 Freeman: 0. 25