TRAVERSING NEWS WITH ANT COLONY OPTIMISATION & NEGATIVE PHEROMONES
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TRAVERSING NEWS WITH ANT COLONY OPTIMISATION & NEGATIVE PHEROMONES David Sousa-Rodrigues 1 , and Vitorino Ramos 2 ECCS’14, Lucca, IT September 22-26, 2014 1 – The Open University, UK - [email protected] 2 – LaSEEB, ISR, Technical University of Lisbon – [email protected]
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The past decade has seen the rapid development of the online newsroom. News published online are the main outlet of news surpassing traditional printed newspapers. This poses challenges to the production and to the consumption of those news. With those many sources of information available it is important to find ways to cluster and organise the documents if one wants to understand this new system. Traditional approaches to the problem of clustering documents usually embed the documents in a suitable similarity space. Previous studies have reported on the impact of the similarity measures used for clustering of textual corpora [1]. These similarity measures usually are calculated for bag of words representations of the documents. This makes the final document-word matrix high dimensional. Feature vectors with more than 10,000 dimensions are common and algorithms have severe problems with the high dimensionality of the data. A novel bio inspired approach to the problem of traversing the news is presented. It finds Hamiltonian cycles over documents published by the newspaper The Guardian. A Second Order Swarm Intelligence algorithm based on Ant Colony Optimisation was developed [2, 3] that uses a negative pheromone to mark unrewarding paths with a “no-entry” signal. This approach follows recent findings of negative pheromone usage in real ants [4]. In this case study the corpus of data is represented as a bipartite relation between documents and keywords entered by the journalists to characterise the news. A new similarity measure between documents is presented based on the Q- analysis description [5, 6, 7] of the simplicial complex formed between documents and keywords. The eccentricity between documents (two simplicies) is then used as a novel measure of similarity between documents. The results prove that the Second Order Swarm Intelligence algorithm performs better in benchmark problems of the travelling salesman problem, with faster convergence and optimal results. The addition of the negative pheromone as a non-entry signal clearly improved the quality of the solutions. The application of the algorithm to the corpus of news of The Guardian creates a coherent navigation system among the news. This allows the users to navigate the news published during a certain period of time in a semantic sequence instead of a time sequence. This work as broader application as it can be applied to many cases where the data is mapped to bipartite relations (e.g. protein expressions in cells, sentiment analysis, brand awareness in social media, routing problems), as it highlights the connectivity of the underlying complex system.
Transcript of TRAVERSING NEWS WITH ANT COLONY OPTIMISATION & NEGATIVE PHEROMONES
TRAVERSING NEWS WITH ANT COLONY OPTIMISATION & NEGATIVE PHEROMONES David Sousa-Rodrigues1, and Vitorino Ramos2
ECCS’14, Lucca, IT September 22-26, 2014 1 – The Open University, UK - [email protected] !2 – LaSEEB, ISR, Technical University of Lisbon – [email protected] ! !
Motivation: Scotland Referendum
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Outline • Context of this work and Related Work
• Newspapers • Adaptive Networks • Q-analysis • Community detection • Ant Colony Optimisation
• Hybrid Connectivity Based Approaches • TSP Paths in Q-analysis eccentricity matrices
• Conclusions
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CONTEXT
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Context: newspapers (print)
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• UK Circulation
/ 25
Context: newspapers (electronic)
Internet traffic Internet overtakes print as news outlet
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Document analysis • Categorisation of documents (Supervised)
• Machine learning • K-neighbours, SVM, NN, …
• Clustering (unsupervised) • Document navigation
• Sometimes associated with clustering • Information retrieval • Topic Modelling (LDA...)
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Network Science • Adaptive Networks
• (interplay of topology dynamics and local dynamics of networks)
• Community Detection in Graphs • Clustering nodes of graphs
• Q-analysis (Hypernetworks) • Topological description of the high dimensionality of
structures.
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Bio-inspired & Artificial Life • Swarm Intelligence algorithms
• Ant Systems • Ant Colony Optimisation • Travelling Salesman Problem
• Anti-pheromone ideas • subtractive anti-pheromone (SAP)
• 1 pheromone – subtracted from poor solutions • preferential anti-pheromone (PAP)
• 2 pheromones but to solve bi-criterion optimisation problems
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TRAVERSING NEWS WITH ANT COLONY OPTIMISATION AND NEGATIVE PHEROMONES
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Two threads • An extension to the Travelling Salesman Problem
algorithm ACS.
• Application of Q-analysis eccentricity matrices as distance matrices in the construction of paths in the directed TSP problem.
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[Rodrigues et al. 2013]
[Rodrigues, 2014]
Travelling Salesman Problem
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[Sousa-Rodrigues, 2011] [Visco, 2005]
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2nd Order Swarm Intelligence • Pharaoh ants (Monomorium pharaonis) deposit a
pheromone as a 'no entry' signal to mark unrewarding foraging paths.
• Double Pheromone Model on top of traditional ACS. • Traditional positive reinforcement pheromone • Use of Negative Pheromone to block bad paths.
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[Robinson:2007]
[Dorigo:1996]
State Transition Rule
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Results – Static problems
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Influence of negative pheromone
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Application to dynamic problems: recovery patterns
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Application to the News • Articles are represented by a bipartite graph:
• < Document, Tags >
• Documents are connected by their shared vertices (Tags)
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Share a vertex
0 - near
Share an edge
1 - near Share a triangle
2 - near
[Johnson, 2014]
Application to the News
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A navigation of q-related news • Scotland Referendum
• Dataset from GDELT project [http://www.gdeltproject.org/] • Filtered for events mentioning Scotland during September
• ~ 700 stories / day mentioning Scotland
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A navigation of q-related news
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A navigation of q-related news
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CONCLUSIONS
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Conclusions • Bio-inspired algorithm for TSP problems (adaptable to
many problems) – improves on past ACS algorithm. • Integration of direct information from the simplex structure
into the algorithm for solution finding. • Creation of coherent corpus of news stories that can be
read in a non timely order but still being highly related.
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References
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References • D. Sousa-Rodrigues, J. Louçã, and V. Ramos, “From standard to second-order swarm
intelligence phase-space maps,” in 8th European Conference on Complex Systems (S. Thurner, ed.), (Vienna, Austria), 9 2011.
• V. Ramos, D. Sousa-Rodrigues, and J. Louçã, “Spatio-temporal dynamics on co-evolved stigmergy,” in 8th European Conference on Complex Systems (S. Thurner, ed.), (Vienna, Austria), 9 2011.
• V. Ramos, D. Sousa-Rodrigues, and J. Louçã, “Second order swarm intelligence,” in HAIS’13. 8th Inter- national Conference on Hybrid Artificial Intelligence Systems (J.-S. Pan, M. Polycarpou, M. Wózniak, A. Carvalho, H. Quintían, and E. Corchado, eds.), vol. 8073 of Lecture Notes in Computer Science, pp. 411–420, Salamanca, Spain: Springer Berlin Heidelberg, 9 2013.
• D. Sousa-Rodrigues, “Identifying news clusters using q-analysis and modularity,” in Proceedings of the European Conference on Complex Systems 2013 (A. Diaz-Guilera, A. Arenas, and Á. Corral, eds.), (Barcelona), 9 2013.
• J. Johnson, Hypernetworks in the Science of Complex Systems. IMPERIAL COLLEGE PRESS, Jan. 2014.
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