A5022197586- Complex Network Analysis Techniques
- Opinion Dynamics and Social Influence
- Data Visualization and Analytics
- Mental Health Research Topics
- Advanced Text Analysis Techniques
- Network Traffic and Congestion Control
- COVID-19 epidemiological studies
- Markov Chains and Monte Carlo Methods
- Software System Performance and Reliability
- Misinformation and Its Impacts
- Traffic Prediction and Management Techniques
- Network Security and Intrusion Detection
- Human Mobility and Location-Based Analysis
- Opportunistic and Delay-Tolerant Networks
- Stochastic processes and statistical mechanics
The University of Adelaide
2018-2020
ARC Centre of Excellence for Mathematical and Statistical Frontiers
2018-2019
Pinpointing autonomous systems which deploy specific inter-domain techniques such as Route Flap Damping (RFD) or Origin Validation (ROV) remains a challenge today. Previous approaches to detect per-AS behavior often relied on heuristics derived from passive and active measurements. Those heuristics, however, lacked accuracy imposed tight restrictions the measurement methods.
Contagion processes are strongly linked to the network structures on which they propagate, and learning these is essential for understanding intervention complex such as epidemics (mis)information propagation. However, using contagion data infer structure a challenging inverse problem. In particular, it imperative have appropriate measures quantify uncertainty in estimates; however, largely ignored many optimisation based approaches. We present probabilistic framework samples from...
Modelling information cascades over online social networks is important in fields from marketing to civil unrest prediction, however the underlying network structure strongly affects probability and nature of such cascades. Even with simple cascade dynamics large are almost entirely dictated by properties, well-known as Erdos-Renyi Barabasi-Albert producing wildly different same model. Indeed, notion 'superspreaders' has arisen describe highly influential nodes promoting global a network....
Abstract Sampling random graphs is essential in many applications, and often algorithms use Markov chain Monte Carlo methods to sample uniformly from the space of graphs. However, there a need with some property that we are unable, or it too inefficient, using standard approaches. In this article, interested sampling conditional ensemble underlying graph model. We present an algorithm generate samples connected Metropolis–Hastings framework. The extends general framework for known...
Tweet clustering for event detection is a powerful modern method to automate the real-time of events. In this work we present new tweet approach, using probabilistic approach incorporate temporal information. By analysing distribution time gaps between tweets show that pairs related exhibit exponential decay, whereas unrelated are approximately uniform. Guided by insight, use arguments estimate likelihood pair related, and build an improved method. Our Social Media Event Response Clustering...