Outputs (2)

Efficient Comparison of Massive Graphs Through The Use Of 'Graph Fingerprints' (2016)
Presentation / Conference Contribution
Bonner, S., Brennan, J., Theodoropoulos, G., Kureshi, I., & McGough, A. (2016, August). Efficient Comparison of Massive Graphs Through The Use Of 'Graph Fingerprints'. Presented at Twelfth Workshop on Mining and Learning with Graphs (MLG) at KDD'16., San Francisco, USA

The problem of how to compare empirical graphs is an area of great interest within the field of network science. The ability to accurately but efficiently compare graphs has a significant impact in such areas as temporal graph evolution, anomaly dete... Read More about Efficient Comparison of Massive Graphs Through The Use Of 'Graph Fingerprints'.

SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder (2016)
Presentation / Conference Contribution
Al Moubayed, N., Breckon, T., Matthews, P., & McGough, A. (2016, August). SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder

In This paper we present a novel approach to spam filtering and demonstrate its applicability with respect to SMS messages. Our approach requires minimum features engineering and a small set of labelled data samples. Features are extracted using topi... Read More about SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder.