Neave O’Clery from the Centre for Advanced Spatial Analysis, University College London will give a live talk on Thursday, October 13 at 15 pm in the Salon.
If you would like to attend, please email to email@example.com.
Title: “A bi-directional approach to comparing the modular structure of networks”
There exist a relative lack of sophisticated methods to compare the network topology of networks. Here we propose a new method to compare the modular structure of a pair of node-aligned networks. The majority of current methods, such as normalized mutual information, compare two node partitions derived from a community detection algorithm yet ignore the respective underlying network topologies. Addressing this gap, our method deploys a community detection quality function to assess the fit of each node partition with respect to the other network’s connectivity structure. Specifically, for two networks A and B, we project the node partition of B onto the connectivity structure of A. By evaluating the fit of B’s partition relative to A’s own partition on network A (using a standard quality function), we quantify how well network A describes the modular structure of B. Repeating this in the other direction, we obtain a two-dimensional distance measure, the bi-directional (BiDir) distance. The advantages of our methodology are three-fold. First, it is adaptable to a wide class of community detection algorithms that seek to optimize an objective function. Second, it takes into account the network structure, specifically the strength of the connections within and between communities, and can thus capture differences between networks with similar partitions but where one of them might have a more defined or robust community structure. Third, it can also identify cases in which dissimilar optimal partitions hide the fact that the underlying community structure of both networks is relatively similar. We illustrate our method for a variety of community detection algorithms, including multi-resolution approaches, and a range of both simulated and real world networks.
Neave O’Clery is Associate Professor and Director of Research at the Centre for Advanced Spatial Analysis (CASA) at University College London where she leads an inter-disciplinary research group focused on network and data-driven models for economic development and urban systems. She is also a Turing Fellow at the Alan Turing Institute, as well as a Visiting Fellow at the Oxford Mathematical Institute and an Oxford Martin Fellow. Her work spans a number of topics and fields including structural change and industrial development, economic complexity and evolutionary economic geography, the informal economy, urban mobility and segregation, and network science. She also works alongside a number of policy and government institutions ranging from city majors to global multi-laterals including the Greater Manchester Combined Authority, the Irish Department for Enterprise, Trade and Employment, and the World Bank. Neave was previously a Senior Research Fellow at the Mathematical Institute at the University of Oxford, and before this a Fulbright Scholar and Postdoctoral Research Fellow at the Center for International Development at the Harvard Kennedy School. She is founder and co-chair of the Oxford Summer School in Economic Networks, a bi-annual multi-disciplinary school for over 100 postgraduate students. She holds a PhD (mathematics) from Imperial College, and was founder and Editor in Chief of Angle – a journal based at Imperial College focusing on the intersection of policy, politics and science – between 2009-2020.