Network Science Mixing Patterns - example Assortativity Index - Denition Cheatsheet Example of mixing patterns of age in a network of interaction be- When the property for which we study homophily is categorical, tween individuals, reproduced froma. homophily can be deneda by comparing the fraction of edges that connect nodes of the same category, and the expected value Made by of such edges if the network was random. More formally, it is ex- Remy Cazabet pressed as: P P 2 eii − a r = i i i P 2 1 − i ai

where eii is the fraction of edges connecting two nodes of cat- Assortativity egory i, and ai the fraction of all edges connected to a node of category i (sum of degrees divided by number of edges).

aNewman 2003. Assortativity - Homophily - Mixing Pat- terns Assortativity index - Example A network is said to be assortative or to demonstrate homophily if its nodes tend to connect more with other nodes that are similar Let’s see a ctional example of how to compute the assortativity than to nodes that are dierent. We can see that there is some level of assortativity (high values index. Nodes are individuals, edges represent for instance some Similarity in this case must be understood in term of nodes prop- on the diagonal), but that there are also some more complex social interaction. Columns/Rows correspond to blood types, and erties. Some typical examples can be age, gender, language, po- mixing patterns, for instance between age 10 and 40, numbers are expressed in fraction of the total number of edges. litical beliefs, etc. approximately, here interpreted as child-parents relationships. Blood Types A AB B O ai Homophily is considered a common feature of many networks, in A 0.30 0.05 0.1 0.05 0.5 a a particular social networks , as reected in the aphorism Birds of a Del Valle et al. 2007. AB 0.05 0.05 0 0 0.1 feather ock together. B 0.1 0 0.2 0 0.3 Typical examples would be age, gender, ethnicity or politicla opin- O 0.05 0 0 0.05 0.1 ions in social networks networks such as Twitterb ai 0.5 0.1 0.3 0.1 1 a McPherson, Smith-Lovin, and Cook 2001. 2 2 2 2 (0.3+0.05+0.2+0.05)−(0.5 +0.1 +0.3 +0.1 ) 0.6+0.36 b r = = = McPherson, Smith-Lovin, and Cook 2001. 1−(0.52+0.12+0.32+0.12) 1−0.36 0.375 Note on interpreting homophily Homophily can be a link creation mechanism (nodes have a pref- Asortativity index - Properties Disassortativity - Heterophily erence to connect with similar ones, so the network end up to be assortative), or a consequence of inuence phenomenons (be- An assortativity index of r = 0 means that the network has no cause nodes are connected, they tend to inuence each other and Some networks can also demonstrate heterophily, or disassorta- assortative mixing, r = 1 corresponds to a perfectly assortative thus become more similar). tivity, i.e., a greater number of connections with nodes that are dif- network (edges exist only between nodes of the same category), Without access to the dynamic of the network and its properties, ferent (for instance, in a sentimental relationship network, women and r = −1 to a perfectly disassortative network (no edge be- it is not possible to dierentiate those eects. tend to connect more with men than with other women, and re- tween nodes of the same category). ciprocally).

Assortativity and Modularity Mixing Patterns Assortativity is related to the Modularity, a measure of the quality Categorical or Numerical homophily of communities, by the following relation: The notion of nodes connecting to each other with preferences Q based on their attributes can be generalized to the concept of Attributes of nodes can be either categorical (no natural order be- r = Qmax Mixing Patterns. Beyond homophily/heterophily, nodes with tween values, discrete number of possible values), or numerical P P 2 property p1 can be preferentially connected to nodes with prop- (natural order, discrete or continuous). Although the general idea Indeed, i eii − i ai corresponds to the denition of the Mod- P 2 erty p2 (and not p3 or p4) while nodes having property p3 can have remains the same, the way to compute homophily diers accord- ularity, while 1 − i ai corresponds to the maximal value that the a preference for nodes having the same property, for instance. ing to type of attributes we are interested in. Modularity could reach if all nodes were in the same communities. Homophily for numeric variables Going Further

When the property for which we study homophily is numeric, ho- A survey on the topic (Noldus and Van Mieghem 2015) mophily r can be dened as the Pearson Correlation Coecient between values at both end of each edge. For details, see New- man 2003. Limits of Assortativity

A limit of assortativity coecients as we have dened them is that Numeric Assortativity index - Properties they summarize the whole network as a single value. However, References dierent parts of the network might have dierent types of assor- Homophily r = 0 means that the network has no assortative mix- tativity. [1] Sara Y Del Valle et al. “Mixing patterns between age groups ing, r > 0 corresponds to an assortative network (nodes with high in social networks”. In: Social Networks 29.4 (2007), pp. 539– values tend to connect to high values), and r < 0 to a disassorta- tive network (nodes with high values are preferably connected to 554. low values). [2] Miller McPherson, Lynn Smith-Lovin, and James M Cook. “Birds of a feather: Homophily in social networks”. In: Annual Degree assortativity review of sociology 27.1 (2001), pp. 415–444. [3] Mark EJ Newman. “Mixing patterns in networks”. In: Physical Degree assortativitya, sometimes simply called assortativity, is a review E 67.2 (2003), p. 026126. particular case of homophily measured in term of node degrees, i.e., the numerical value associated to each node is its degree. [4] Rogier Noldus and Piet Van Mieghem. “Assortativity in com- The existence of a degree assortativity can be interpreted in term Illustration of dierent local assortativity behaviors leading to the of a rich club phenomenon: hubs prefer to connect to other hubs. same global assortativity value (bottom: distribution of local plex networks”. In: Journal of Complex Networks 3.4 (2015), ER, Conguration and BA random graph models have a degree assortatvity). Figure froma, in which the authors propose a pp. 507–542. assortativity equals to 0, while many real networks have positive measure of multiscale assortativity. values, and some negative ones. [5] Leto Peel, Jean-Charles Delvenne, and Renaud Lambiotte. aPeel, Delvenne, and Lambiotte 2018. “Multiscale mixing patterns in networks”. In: Proceedings of aNewman 2003. the National Academy of Sciences 115.16 (2018), pp. 4057– 4062.