University of Illinois at Urbana - ChampaignDept . of Electrical and Computer Engineering​​

ECE 101 : Exploring Digital Information Technologies for Non-Engineers

Lecture 9: Social Networks

9.
1
Some Stats

Reference: https://datareportal.com/social-media-users

9.
2
Some Stats

Reference: https://datareportal.com/social-media-users

9.
3
What is Social Media?

◼
  • Online platforms that allow users to create, share, and interact with content and each other
    • ◼
  • Has become an integral part of our lives, influencing how we communicate, access information, and even perceive the world

    • 9.
    4
    Digital Mechanisms of Social Media

    ◼
  • User Profiles and Content Creation
    • ◼
  • Networking and Connectivity
    • ◼
  • Algorithms and Personalization
    • ◼
  • Privacy and Data Security

    • 9.
    5
    Social Networks on Social Media

    Graph Recap

    A graph is a collection of objects sharing some form of relationship. These objects are referred to as vertices (or nodes) and the relationships as edges (or links).

    Undirected, unweighted graph

    Out[]=

    Directed, unweighted graph

    Out[]=

    Undirected, weighted graph

    Out[]=

    Directed, weighted graph

    Out[]=

    Graph v.s Network

    Examples of Networks

    9.
    6
    Social Networks represented by Graphs

    Graphs can be used to represent social networks
    ◼
  • Vertices are people
    • ◼
  • Edges are relationships
    • In[]:=
    ExampleData[{"NetworkGraph","FamilyGathering"},"LongDescription"]
    In[]:=
    fg=ExampleData[{"NetworkGraph","FamilyGathering"}]

    9.
    7
    Graph Properties

    Graphs have properties that convey useful information.
    In[]:=
    fg
    Number of vertices (number of people in this family):
    In[]:=
    VertexCount[fg]
    Number of edges (number of distinct relationships):
    In[]:=
    EdgeCount[fg]
    List all the vertices (members of the family):
    In[]:=
    VertexList[fg]
    List all the edges (the relationships):
    In[]:=
    EdgeList[fg]
    In[]:=
    fg
    The vertex degree for a vertex
    v
    is the number of edges incident to
    v
    .
    List the number of edges incident to each vertex:
    In[]:=
    VertexDegree[fg]
    In[]:=
    VertexList[fg]
    Number of edges associated with a particular vertex:
    In[]:=
    VertexDegree[fg,"Anna"]
    In[]:=
    VertexDegree[fg,"Oscar"]
    Find the set of vertices with maximum vertex degree:
    In[]:=
    GraphHub[fg]
    In[]:=
    fg
    The greatest distance between any pair of vertices:
    In[]:=
    GraphDiameter[fg]
    Shortest path between two vertices:
    In[]:=
    GraphDistance[fg,"James","Felicia"]
    In[]:=
    FindShortestPath[fg,"James","Felicia"]
    In[]:=
    GraphDistance[fg,"Felicia","Rudy"]
    In[]:=
    FindShortestPath[fg,"Felicia","Rudy"]
    Cliques are special graphs. Each vertex is connected to every other vertex.
    In[]:=
    c=FindClique[fg]
    In[]:=
    fg
    In[]:=
    HighlightGraph[fg,Subgraph[fg,c]]

    9.
    8
    Small World Graph

    Human relationships—the social graph—form a type of graph now called a small-world graph, named after the expression, “It’s a small world!”
    Small world graphs have certain properties:
    ◼
  • small diameter and typical path length,
    • ◼
  • local cliques,
    • ◼
  • densely connected,
    • ◼
  • heavy tail

    • Small Diameter

    Is our family graph an example of a small world graph?

    Six Degrees of Separation

    Popular ideas about six degrees of separation:
    ◼
  • Six degrees of Kevin Bacon
    • ◼
  • Erdos number

    • The Experiment

    Should we play the game “Six Degrees of Kevin Bacon”? (If there is time)

    Social networks inherit properties of small world graphs and capture human relationship
    ◼
  • local cliques (a person’s friends tend to know one another)
    • ◼
  • densely connected (most of the people have lots of connections)
    • ◼
  • heavy tails (some nodes have very high degree--the influencers--while other nodes have a reasonable number of connections)
    • Two pillars of digital technologies as discussed in this course: Information and Computation

    Communities

    Graph Mining

    Your turn ... Class Participation Question

    How do companies store and provide access to a social graph?

    In case you are interested about this picture

    Facebook’s TAO architecture (perhaps “Total Association and Object”)

    Each Node and Associated Edges Map into One Piece

    Full Copy of Graph in Each of a Few Datacenters

    Datacenters Provide Data Fast at Expense of Consistency

    ◼
  • Algorithms analyze user behavior to curate personalized content feeds.
    • ◼
  • Collect information about your interactions and preferences
    • ◼
  • track various interactions such as likes, comments, shares, and time spent on posts
    • ◼
  • monitor how users engage with different types of content (e.g., videos, photos, articles) and which formats keep users engaged longer.
    • ◼
  • consider your search queries and the profiles or pages you visit.
    • ◼
  • data from your device and location to tailor content relevant to your geographical area
    • ◼
  • Personalization: Based on the collected data, algorithms rank content to prioritize what appears at the top of your feed.
    • ◼
  • Recommendations: Suggest new content, profiles, or pages to follow based on your interests and the behavior of similar users.
    • ◼
  • Targeted ads: Based on your interactions and preferences. E.g., if you frequently engage with fitness content, you might see ads for workout gear or health supplements.
    • https://app.sli.do/event/54TEjkWUTSFpEpBdW6Mc18

    Fake News Travels the Network

    Impact on communication

    Mental health

    Influencers on Social Networks command undue “Influence”

    ◼
  • Graph vs. Network
    • ◼
  • Social network represented as a graph
    • ◼
  • Properties of a small world graph
    • ◼
  • Properties of social network that make it a small world graph
    • ◼
  • vertices (or nodes) and edges (or links)
    • ◼
  • Vertex degree, graph diameter, graph hub, shortest path
    • ◼
  • Applications of computing on social network: Communities and Shopping Recommendations
    • ◼
  • Issues with storing a large network graph and solutions; TAO
    • ◼
  • Machine Learning for personalization and recommendations
    • ◼
  • How algorithms shape what you see on social media from the Today show
    • ◼
  • How TikTok's Algorithm Figures You Out from WSJ
    • ◼
  • American's Social Media Use by the Pew Research Center
    • ◼
  • Analyzing the Social Web by Jennifer Golbeck