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Spread of Acceptance of Gays and Lesbians

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Using graph theory to spread the acceptance of gays and lesbians in society.

Using graph theory to spread the acceptance of gays and lesbians in society.

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  • 1. Spread of Acceptance of Gay and Lesbian Relationships
    Angela* and Dorea Vierling-Claassen
    Lesley University
    Community of Scholars Day
    March 30, 2011
  • 2. Acceptance of Homosexuality
    According to Gallup polls, the percentage of Americans who believe it is “morally acceptable” to be gay or lesbian has risen from 40% to 52% over the last 10 years.
    What is driving the increasing acceptance levels?
    One factor is relationships – a relationship with a gay or lesbian person interacts with a person’s prior beliefs and attitudes and is a powerful force to change one mind a time.
    How can math help us understand this?
    We can use math to model human interactions and social networks.
  • 3. Complex Networks
    In recent years, there has been an explosion of interest in complex networks, which can model
    Social networks, such as friendships or collaborative networks
    Food webs, neural networks, and other biological networks
    The internet, power grids, subway systems, and other constructed networks
    Complex networks have an irregular structure.
    Evolve dynamically in time
    General trend is to move from considering small networks to networks with thousands or millions of nodes.
  • 4. Networks: the Math
    Mathematically, networks are graphs, which are made up of nodes and edges
    Once we have a graph, we can talk about characteristics of nodes and of the graph as a whol
  • 5. Characteristics
    Degree of a node, which means a nodes total number of neighbors (other nodes connected to it by an edge)
    Clustering coefficient – how likely are two neighbors of any node to be neighbors of each other (do my friends know each other?)
  • 6. Nodes can have attributes
  • 7. Edges can have attributes
  • 8. Modeling Acceptance
    We start by assigning each node a random level of acceptance between 0 and 1, with higher numbers being more accepting.
    We reserve an acceptance level of 1 to indicate that a node is gay or lesbian.
    Over time, we allow interactions between the nodes to change the acceptance level of the nodes. However, gay and lesbian nodes always have an acceptance level of 1.
    Edges between nodes are classified as “friend” or “family”
  • 9. How Minds are Changed
    Randomly select a node N and friend or family member F.
    If the acceptance levels of N and F are close enough to each other, both N and F move closer to their average level.
    If the acceptance level is too far apart, the relationship is severed, the edge is removed, and N is randomly assigned a new friend by creating a new edge.
  • 10. Some Notes
    Family ties are harder to break that friend ties (they will tolerate a larger degree of difference)
  • 11. What Happens Over Time (3000 steps)
    Start
    End
  • 12. Evolution of Queer vs. Straight Ties
    Connections between gay nodes
    Connections between similar collection of straight nodes.
  • 13. Impact of Non-Acceptance
    When the average network acceptance level is low, gays and lesbians cluster into communities that are somewhat separate from straight communities
  • 14. Impact of Non-Acceptance
    Such networks end up having two separate clusters – queers and straights
    Queers are highly connected with other queers
    Queers have fewer total connections (because many straight people have broken ties)
  • 15. Impact of Weak Ties
    If all ties are easy to break, network becomes more unaccepting over time
  • 16. Impact of Strong Ties
    Having strong ties (such as family ties) that resist breaking leads to a more accepting network over time.
    This is true even if network is unaccepting to begin with.
  • 17. Networks which Include Acceptance
    Here, all ties are relatively easy to break. The same phenomena occurs when we include family ties (harder to break).
  • 18. Impact of Family Ties
    Increasing the number of ties that are difficult to break will pull the network toward more acceptance.
    However, if a network starts out unaccepting, then even when it eventually becomes more accepting the clustering of gay people persists in this model.
  • 19. Preliminary Conclusions
    Ties that are difficult to break change the overall acceptance level, so keep those ties!
    Counteracting the clumping effects and social isolation of queers – interested in working on discovery of ways to do this through the model (without forcing an accepting network to start). If you have ideas, I’m all ears.
    Some models lead toward everyone eventually being accepting – some end up with several clusters of opinions ranging from positive to negative.
  • 20. Next Steps
    Modeling is currently implemented on a random network. One next step is to implement the model with a network that better models a social network, such as a “small world” type graph.
    Connect with research on real social networks that include gays and lesbians – there is not much of this currently in existence.
    Work on modeling what happens to an existing network with a mix of opinions when a person comes out. Can include in this mix both the impact of coming out and the likelihood of a person coming out based on opinions of connections.