arXiv subjects

Richard Clegg

Publications and source records attributed to Richard Clegg.

7 recordsLinked to original sources

arXiv2026

A model for generating temporal networks with dynamic community structure guided by mutual information

This paper introduces a generative model for temporal networks that jointly controls community evolution and dynamic node sets. The model represents community structure as a sequence of partitions and uses a genetic search guided by a similarity measure based on mutual information to regulate changes between snapshots. This allows explicit control of community evolution including splits and merges while handling node additions and removals. Temporal edges are then generated using intra- and inter-community probabilities derived from data or theoretical bounds to ensure connectivity. Simulation experiments on real-world datasets demonstrate the ability of the generative model to model the evolution of real dynamic communities. The model is used as a benchmark to study the impact of the rate at which nodes join/leave the network on the performance of dynamic community detection algorithms.

cs.SI↗

arXiv2025

Mining a Decade of Event Impacts on Contributor Dynamics in Ethereum: A Longitudinal Study

We analyze developer activity across 10 major Ethereum repositories (totaling 129884 commits, 40550 issues) spanning 10 years to examine how events such as technical upgrades, market events, and community decisions impact development. Through statistical, survival, and network analyses, we find that technical events prompt increased activity before the event, followed by reduced commit rates afterwards, whereas market events lead to more reactive development. Core infrastructure repositories like Go-Ethereum exhibit faster issue resolution compared to developer tools, and technical events enhance core team collaboration. Our findings show how different types of events shape development dynamics, offering insights for project managers and developers in maintaining development momentum through major transitions. This work contributes to understanding the resilience of development communities and their adaptation to ecosystem changes.

cs.SE↗

arXiv2024

Quantifying community evolution in temporal networks

When we detect communities in temporal networks it is important to ask questions about how they change in time. Normalised Mutual Information (NMI) has been used to measure the similarity of communities when the nodes on a network do not change. We propose two extensions namely Union-Normalised Mutual Information (UNMI) and Intersection-Normalised Mutual Information (INMI). UNMI and INMI evaluate the similarity of community structure under the condition of node variation. Experiments show that these methods are effective in dealing with temporal networks with the changes in the set of nodes, and can capture the dynamic evolution of community structure in both synthetic and real temporal networks. This study not only provides a new similarity measurement method for network analysis but also helps to deepen the understanding of community change in complex temporal networks.

cs.SI↗

arXiv2021

Internet Traffic Volumes Are Not Gaussian -- They Are Log-Normal: An 18-Year Longitudinal Study With Implications for Modelling and Prediction (Complete Version)

Getting good statistical models of traffic on network links is a well-known, often-studied problem. A lot of attention has been given to correlation patterns and flow duration. The distribution of the amount of traffic per unit time is an equally important but less studied problem. We study a large number of traffic traces from many different networks including academic, commercial and residential networks using state-of-the-art statistical techniques. We show that traffic obeys the log-normal distribution which is a better fit than the Gaussian distribution commonly claimed in the literature. We also investigate an alternative heavy-tailed distribution (the Weibull) and show that its performance is better than Gaussian but worse than log-normal. We examine anomalous traces which exhibit a poor fit for all distributions tried and show that this is often due to traffic outages or links that hit maximum capacity. We demonstrate that the data we look at is stationary if we consider samples of 15- minute long or even 1-hour long. This gives confidence that we can use the distributions for estimation and modelling purposes. We demonstrate the utility of our findings in two contexts: predicting that the proportion of time traffic will exceed a given level (for service level agreement or link capacity estimation) and predicting 95th percentile pricing. We also show that the log-normal distribution is a better predictor than Gaussian or Weibull distributions in both contexts.

cs.NI↗

arXiv2018

Finding Dory in the Crowd: Detecting Social Interactions using Multi-Modal Mobile Sensing

Remembering our day-to-day social interactions is challenging even if you aren't a blue memory challenged fish. The ability to automatically detect and remember these types of interactions is not only beneficial for individuals interested in their behavior in crowded situations, but also of interest to those who analyze crowd behavior. Currently, detecting social interactions is often performed using a variety of methods including ethnographic studies, computer vision techniques and manual annotation-based data analysis. However, mobile phones offer easier means for data collection that is easy to analyze and can preserve the user's privacy. In this work, we present a system for detecting stationary social interactions inside crowds, leveraging multi-modal mobile sensing data such as Bluetooth Smart (BLE), accelerometer and gyroscope. To inform the development of such system, we conducted a study with 24 participants, where we asked them to socialize with each other for 45 minutes. We built a machine learning system based on gradient-boosted trees that predicts both 1:1 and group interactions with 77.8% precision and 86.5% recall, a 30.2% performance increase compared to a proximity-based approach. By utilizing a community detection-based method, we further detected the various group formation that exist within the crowd. Using mobile phone sensors already carried by the majority of people in a crowd makes our approach particularly well suited to real-life analysis of crowd behavior and influence strategies.

cs.HC↗

arXiv2017

Extending programs with debug-related features, with application to hardware development

The capacity and programmability of reconfigurable hardware such as FPGAs has improved steadily over the years, but they do not readily provide any mechanisms for monitoring or debugging running programs. Such mechanisms need to be written into the program itself. This is done using ad hoc methods and primitive tools when compared to CPU programming. This complicates the programming and debugging of reconfigurable hardware. We introduce Program-hosted Directability (PhD), the extension of programs to interpret direction commands at runtime to enable debugging, monitoring and profiling. Normally in hardware development such features are fixed at compile time. We present a language of directing commands, specify its semantics in terms of a simple controller that is embedded with programs, and implement a prototype for directing network programs running in hardware. We show that this approach affords significant flexibility with low impact on hardware utilisation and performance.

cs.PL↗

arXiv2009

Evaluating and Optimising Models of Network Growth

This paper presents a statistically sound method for measuring the accuracy with which a probabilistic model reflects the growth of a network, and a method for optimising parameters in such a model. The technique is data-driven, and can be used for the modeling and simulation of any kind of evolving network. The overall framework, a Framework for Evolving Topology Analysis (FETA), is tested on data sets collected from the Internet AS-level topology, social networking websites and a co-authorship network. Statistical models of the growth of these networks are produced and tested using a likelihood-based method. The models are then used to generate artificial topologies with the same statistical properties as the originals. This work can be used to predict future growth patterns for a known network, or to generate artificial models of graph topology evolution for simulation purposes. Particular application examples include strategic network planning, user profiling in social networks or infrastructure deployment in managed overlay-based services.

cs.NI↗