This post is meant to be very approachable and an academic background is not expected. However, it has an academic flavor and deals with applied theory, in the same vein as previous posts on lambda calculus and parallel computing.
This is a post about graphing social relationships. These techniques are used for advertisement, propaganda, predicting disease spread, predicting future relationships (as in LinkedIn “you might know” suggestions), predicting ideology or opinion, and a variety of other tasks. Network science is widely used in academia, in “big data” corporations, and by governments. This post will serve as a brief crash course into network science through the lens of social media scraping, with an emphasis on different models for representing relationships, and their use-cases and shortcomings.
Say we want to identify the most influential members of a community. “Influential” is a ambiguous term, and we could be referring to “the most well-connected individual”, or “the people that bring in ideas from outside the community”, or some notion of a “trend-setter”. To explore any of those definitions, our first task is to identify the members of the community and how they are inter-related.
To start we’ll draw a node (or a vertex if you come from a math/graph-theory background instead of computer/network-science) for Bob. We’ll identify all of Bob’s peers: their friends on Facebook, mutuals on Twitter, contacts on LinkedIn, or whatever parallel makes sense for the platform we’re studying. We’ll create a node for each peer, and we’ll draw an edge between Bob’s node and their friends:
We have our first network (or graph in graph-theory terminology). We can use this network to identify important nodes for the community, which usually involves some of the following characteristics:
The nodes with the most connections (the highest degree)
The bridge nodes that connect two communities together (Bob connects Alice to Dave and Carol)
The central nodes (Betweenness Centrality is a score based on how many fastest routes between any two nodes pass through this node)
We can create a larger network with more meaningful data by looking at Alice, Carol, and Dave’s peers, and building out further and further. The only limits are time and available information from the platform we’re studying.
Not all relationships can be described bidirectionally, like a friendship. For example, Twitter allows one user to follow another without reciprocity. Retweets, likes, and replies are all a form of connection that may or may not be bidirectional. To capture this distinction, we need to add direction to the edges of our graph to create a directed graph or digraph:
This changes the attributes we can measure, but only a little. Instead of degree to indicate how many connections a node has, we now have indegree and outdegree to indicate how many edges lead into and out of the node. This is usually even better, since we can now distinguish users that many people listen to from users that follow many people. Our measurements of bridges and centrality can also utilize direction, tracing only the paths a message can flow from one user to the next through a community.
There may be several attributes that can be thought of as a “relationship”. Returning to the Twitter example again, we have the following relationships:
All of these could be represented as edges on a social graph, but each relationship type has a different implication. Retweets (not including quote-retweets) most strongly indicate “support” and a positive relationship, since one user is rebroadcasting the message of another without commentary. Mentions and replies, on the other hand, could be positive or could indicate arguments and distrust. Follows are also ambiguous, since many users will follow individuals for news purposes, like their politicians, regardless of whether they support those individuals. Volume may also vary significantly between relationship types: Users may “like” messages widely, but retweet or reply to more select content.
Therefore, while we could draw one graph with an edge indicating any of the above relationships, we probably want to separate them. This could mean creating a separate graph for each category of relationship, or it could mean adding edge attributes that indicate which type of relationship each edge refers to. We can also use edge attributes to encode data like the number of retweets, or the age of a response. Comparing the attributes can lead to interesting discoveries, such as identifying a universally despised user that’s frequently mentioned by members of a community but never uncritically retweeted, or a user that used to be regularly retweeted, but has fallen from grace and is no longer amplified.
In addition to metrics for individual nodes, we can take measurements of an entire community. For example the degree distribution illustrates whether a community has about equal engagement, or whether a minority of users massively stand out as being followed more, mentioned more, retweeted more, depending on what the edges represent. We can also define group-level measurements like insularity, indicating what percentage of retweets by users inside of a group are retweeting other members of the group versus retweeting people outside of the group.
Most of these measurements only make sense if we take a much larger network sample, growing from our example diagrams of four users above to tens or hundreds of thousands. The following is one such network graph from Twitter data, created with SocMap:
Of course, community-level analysis requires a clear definition of who is “in” a community and who is not. Sometimes there’s a convenient external data point: If our community is “MIT students and graduates on LinkedIn” then we can define our in-group based on users with MIT in the education section of their profiles with a low degree of error. If our community is “right-wing users” on a platform like Twitter or Facebook then maybe we can create a fuzzy metric that scores users that repeatedly link to right-wing websites or frequently post specific right-wing-affiliated phrases. Highly scored users are likely to be part of the in-group.
Given solely network data there are algorithms for trying to “autodetect” communities based on the assumption that people in a community tend to be linked to other members of the community, but these algorithms are never as reliable as using external data, and frequently depend on analyst-supplied information like the number of communities to split users into.
Networks are constrained by what information is available, and it’s important not to overstate their accuracy. For example, not every friend will be friends on Facebook or connections on LinkedIn, or several users may know one another through a mutual friend that isn’t on the platform. There will almost always be nodes and edges “missing” from a social network graph. Sometimes this missing data is of primary interest! For example, “You may know” suggested connections on LinkedIn are based on a simple algorithm:
Identify 2nd and 3rd degree connections (that is, connections of your connections who you are not connected to)
Sort the potential connections by a combination of:
Shared peers (more connections in common)
Shared place of employment
Shared skills and interests
Determining which attributes are most accurate predictors of a connection to optimize the above algorithm is a more difficult problem, and one LinkedIn has no doubt spent a great deal of time studying.
While networks are valuable tools for predicting patterns of behavior, it’s critical to remember that these network graphs represent only a slice of real-world connections. A snapshot of Twitter misses that many users may connect over Instagram, Facebook, or SMS, and messages spread across these “invisible” edges frequently.
The biggest limitation we’ve seen with graphs so far is that it assumes all relationships involve only two parties. This is frequently appropriate, and accurately describes most phone calls, emails, and text messages. Unfortunately, it’s just as frequently inappropriate: A group chat between three people is not the same as as three two-party conversations between each participant. There may be topics you would discuss in private that you wouldn’t discuss in shared company, or conversely information you would dismiss as a rumor if it were shared with you privately, but seems more believable if it’s shared with your full peer-group. The context of a conversation is critical for understanding how information will be shared or accepted. Further, we can’t even assume that a group context implies members can speak individually: members of a group project may only speak together and never independently.
The simplest way to model these group contexts is to extend our definition of a graph. What if an edge can connect three or more nodes together? We’ll call this a hyperedge to distinguish from traditional edges, and we’ll call graphs containing hyperedges hypergraphs. For now, we can represent a hyperedge as a dotted line encompassing all nodes within it:
Obviously this will be messy to draw with many intersecting hyperedges, but we can perform a lot of mathematical and computer-sciency analysis without visualizing the network we’re working with, so that’s far from a show-stopper.
Note that our example includes only undirected hyperedges. We may also desire a concept of directed hyperedges to represent multicast messages. For example, an emergency hurricane alert broadcast to every cellphone in a city represents a shared message context, but only the emergency service can send messages in the group. Alternatively, consider a Telegram group chat configured as an announcement service, so a dozen or so administrators can write to the group, but thousands can listen. For some types of analysis it may be appropriate to represent these “broadcasts” as many directed edges from the broadcaster to every listener, but if the group context is important to preserve then we need a directed hyperedge to model the conversation space.
Even directed hypergraphs have limitations, but to demonstrate them we’ll need to back up and explain an alternative solution to modeling group context.
The simplicial set represents group relationships with geometry. For example, if a triangle of edges represents three users with independent relationships with one another, then a filled triangle represents three users with a shared relationship with each-other:
If we want to represent a shared relationship between four individuals, we can switch to a tetrahedron (three sided pyramid, or a 3-dimensional version of a triangle). For five individuals, we create a 5-cell, the 4-dimensional equivalent of a triangle, and so on. Higher-dimensionality shapes rapidly become difficult to visualize, but it’s conceptually sound.
Multiple shapes in this geometric space can interact with one another. For example, consider two adjoining triangles:
We can describe DE in two ways. DE can be the shared edge between CDE and DEF, indicating a shared context in that DE is a sub-group that bridges the two larger triangles. However, we can also add an edge between D and E, indicating that they have a relationship outside of this shared bridge space.
Similarly, we can describe a tetrahedron either as the three-dimensional space encompassing four nodes, or as a union of three triangles, or as a combination of triangles and three-space. The difference in phrasing can represent a group of four people or a collaboration between multiple sub-groups.
Sub-grouping and intersection is extremely difficult to describe in a hypergraph. We can create a concept of a hyper-hyperedge which links two hyperedges together to simulate a metagroup, but this is at best an awkward substitute. A hyper-hyperedge still leaves great ambiguity distinguishing separate teams that communicate versus intersections between teams, and making a group that consists of some individuals and some other groups becomes messy very quickly. If we stick to hypergraphs we must content ourselves with representing many group dynamics as footnotes outside of the graph itself, which makes analysis extremely difficult.
Finally, simplicial sets are always directional. We can have multiple congruent but distinct triangles, ABC, BCA, CAB, ACB, and so on, which represent distinct social contexts involving the same three people. We can easily simulate undirected groups using simplicial sets (by sorting all participants before describing a group), but if directionality is desired to represent social hierarchy or multicast communication then the distinction is already built into simplicial group definitions.
Unfortunately, moving from theory to practice is more challenging. Simplicial sets are based on category theory and algebraic geometry, and the math involved reflects that. While there are well-developed software tools for working with undirected and directed graphs, there are few for hypergraphs, and almost none for simplicial sets, limiting present adoption outside of theoretical mathematical spaces.
This post provides an extremely high-level overview of network science within the context of building relationship maps from social media. It’s a flexible discipline, and network analysts spend as much time (if not more) determining which measurements are appropriate and what metrics mean in terms of real-world behavior as they do working through math and code. Because nodes and edges can represent such diverse phenomenon (and this post only scratches the surface without mentioning multi-layer and bipartite networks) most network analysis tools require significant configuration and code from analysts to produce meaningful results.
With that said, some of the versatile libraries used for network analysis in Python include NetworkX, iGraph, and graph-tool. While each library has a limited ability to render networks for visual inspection, most analysts turn to Gephi or (my personal favorite) Cytoscape to explore their networks and display them for publication.
For more on hypergraphs and simplicial sets, I found this paper to be approachable despite lacking any category theory background.