Assortativity

Assortativity, or assortative mixing is a preference for a network's nodes to attach to others that are similar in some way. Though the specific measure of similarity may vary, network theorists often examine assortativity in terms of a node's degree.[1] The addition of this characteristic to network models more closely approximates the behaviors of many real world networks.

Correlations between nodes of similar degree are often found in the mixing patterns of many observable networks. For instance, in social networks, nodes tend to be connected with other nodes with similar degree values. This tendency is referred to as assortative mixing, or assortativity. On the other hand, technological and biological networks typically show disassortative mixing, or disassortativity, as high degree nodes tend to attach to low degree nodes.[2]

MeasurementEdit

 
Fig. 1: Scale-free networks for different degrees of assortativity: (a) A = 0 (uncorrelated network), (b) A = 0.26, (c) A = 0.43, where A indicates r (the assortativity coefficient, as defined in this sub-section).[3]

Assortativity is often operationalized as a correlation between two nodes. However, there are several ways to capture such a correlation. The two most prominent measures are the assortativity coefficient and the neighbor connectivity. These measures are outlined in more detail below.

Assortativity coefficientEdit

The assortativity coefficient is the Pearson correlation coefficient of degree between pairs of linked nodes.[2] Positive values of r indicate a correlation between nodes of similar degree, while negative values indicate relationships between nodes of different degree. In general, r lies between −1 and 1. When r = 1, the network is said to have perfect assortative mixing patterns, when r = 0 the network is non-assortative, while at r = −1 the network is completely disassortative.

The assortativity coefficient is given by  . The term   is the distribution of the remaining degree. This captures the number of edges leaving the node, other than the one that connects the pair. The distribution of this term is derived from the degree distribution   as  . Finally,   refers to the joint probability distribution of the remaining degrees of the two vertices. This quantity is symmetric on an undirected graph, and follows the sum rules   and  .

In a Directed graph, in-assortativity ( ) and out-assortativity ( ) measure the tendencies of nodes to connect with other nodes that have similar in and out degrees as themselves, respectively.[4] Extending this further, four types of assortativity can be considered (see[5]). Adopting the notation of that article, it is possible to define four metrics  ,  ,  , and  . Let  , be one of the in/out word pairs (e.g.  ). Let   be the number of edges in the network. Suppose we label the edges of the network  . Given edge  , let   be the  -degree of the source (i.e. tail) node vertex of the edge, and   be the  -degree of the target (i.e. head) node of edge  . We indicate average values with bars, so that  , and   are the average  -degree of sources, and  -degree of targets, respectively; averages being taken over the edges of the network. Finally, we have

 

Neighbor connectivityEdit

 
Fig. 2: knn distribution for two real-world networks. The top network (a) is disassortative, since the slope is negative. On the other hand, (b) is assortative, since the slope is positive.[6]

Another means of capturing the degree correlation is by examining the properties of  , or the average degree of neighbors of a node with degree k.[7] This term is formally defined as:  , where   is the conditional probability that an edge of node with degree k points to a node with degree k'. If this function is increasing, the network is assortative, since it shows that nodes of high degree connect, on average, to nodes of high degree. Alternatively, if the function is decreasing, the network is disassortative, since nodes of high degree tend to connect to nodes of lower degree. The function can be plotted on a graph (see Fig. 2) to depict the overall assortativity trend for a network.

Local assortativityEdit

In assortative networks, there could be nodes that are disassortative and vice versa. A local assortative measure[8] is required to identify such anomalies within networks. Local assortativity is defined as the contribution that each node makes to the network assortativity. Local assortativity in undirected networks is defined as,

 

Where   is the excess degree of a particular node and   is the average excess degree of its neighbors and M is the number of links in the network.

Respectively, local assortativity for directed networks[4] is a node's contribution to the directed assortativity of a network. A node's contribution to the assortativity of a directed network   is defined as,  

Where   is the out-degree of the node under consideration and   is the in-degree,   is the average in-degree of its neighbors (to which node  } has an edge) and   is the average out-degree of its neighbors (from which node   has an edge). , .

By including the scaling terms   and   , we ensure that the equation for local assortativity for a directed network satisfies the condition  .

Further, based on whether the in-degree or out-degree distribution is considered, it is possible to define local in-assortativity and local out-assortativity as the respective local assortativity measures in a directed network.[4]

Assortative mixing patterns of real networksEdit

 
Fig. 3: Size n and assortativity coefficient r for various networks.[2]

The assortative patterns of a variety of real world networks have been examined. For instance, Fig. 3 lists values of r for a variety of networks. Note that the social networks (the first five entries) have apparent assortative mixing. On the other hand, the technological and biological networks (the middle six entries) all appear to be disassortative. It has been suggested that this is because most networks have a tendency to evolve, unless otherwise constrained, towards their maximum entropy state—which is usually disassortative.[9]

The table also has the value of r calculated analytically for two models of networks:

  1. the random graph of Erdős and Rényi
  2. BA Model (Barabási-Albert model)

In the ER model, since edges are placed at random without regard to vertex degree, it follows that r = 0 in the limit of large graph size. The scale-free BA model also holds this property. For the BA model in the special case of m=1 (where each incoming node attaches to only one of the existing nodes with a degree-proportional probability), we have   as   in the limit of large  .[2]

ApplicationEdit

The properties of assortativity are useful in the field of epidemiology, since they can help understand the spread of disease or cures. For instance, the removal of a portion of a network's vertices may correspond to curing, vaccinating, or quarantining individuals or cells. Since social networks demonstrate assortative mixing, diseases targeting high degree individuals are likely to spread to other high degree nodes. Alternatively, within the cellular network—which, as a biological network is likely dissortative—vaccination strategies that specifically target the high degree vertices may quickly destroy the epidemic network.

Structural disassortativityEdit

The basic structure of a network can cause these measures to show disassortativity, which is not representative of any underlying assortative or disassortative mixing. Special caution must be taken to avoid this structural disassortativity.

See alsoEdit

ReferencesEdit

  1. ^ Newman, M. E. J. (27 February 2003). "Mixing patterns in networks". Physical Review E. American Physical Society (APS). 67 (2): 026126. arXiv:cond-mat/0209450. Bibcode:2003PhRvE..67b6126N. doi:10.1103/physreve.67.026126. ISSN 1063-651X.
  2. ^ a b c d Newman, M. E. J. (28 October 2002). "Assortative Mixing in Networks". Physical Review Letters. American Physical Society (APS). 89 (20): 208701. arXiv:cond-mat/0205405. Bibcode:2002PhRvL..89t8701N. doi:10.1103/physrevlett.89.208701. ISSN 0031-9007. PMID 12443515.
  3. ^ Xulvi-Brunet, R.; Sokolov, I.M. (2005). "Changing correlations in networks: assortativity and dissortativity". Acta Physica Polonica B. 36 (5): 1431.
  4. ^ a b c Piraveenan, M.; Prokopenko, M.; Zomaya, A.Y. (2008). "Assortative mixing in directed biological networks". IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9 (1): 66–78. doi:10.1109/TCBB.2010.80. PMID 20733240.
  5. ^ Foster, Jacob; David V. Foster; Peter Grassberger; Maya Paczuski (June 2010). "Edge direction and the structure of networks". Proceedings of the National Academy of Sciences. 107 (24): 10815–20. arXiv:0908.4288. Bibcode:2010PNAS..10710815F. doi:10.1073/pnas.0912671107. PMC 2890716. PMID 20505119.
  6. ^ Lee, Sang Hoon; Kim, Pan-Jun; Jeong, Hawoong (4 January 2006). "Statistical properties of sampled networks". Physical Review E. American Physical Society (APS). 73 (1): 016102. arXiv:cond-mat/0505232. doi:10.1103/physreve.73.016102. ISSN 1539-3755.
  7. ^ Pastor-Satorras, Romualdo; Vázquez, Alexei; Vespignani, Alessandro (2001). "Dynamical and Correlation Properties of the Internet". Physical Review Letters. American Physical Society (APS). 87 (25): 258701. arXiv:cond-mat/0105161. Bibcode:2001PhRvL..87y8701P. doi:10.1103/physrevlett.87.258701. ISSN 0031-9007. PMID 11736611.
  8. ^ Piraveenan, M.; Prokopenko, M.; Zomaya, A.Y. (2008). "Local assortativeness in scale-free networks". EPL (Europhysics Letters). 84 (2): 28002. Bibcode:2008EL.....8428002P. doi:10.1209/0295-5075/84/28002.
  9. ^ Johnson, Samuel; Torres, Joaquín J.; Marro, J.; Muñoz, Miguel A. (11 March 2010). "Entropic Origin of Disassortativity in Complex Networks". Physical Review Letters. American Physical Society (APS). 104 (10): 108702. arXiv:1002.3286. Bibcode:2010PhRvL.104j8702J. doi:10.1103/physrevlett.104.108702. ISSN 0031-9007. PMID 20366458.