NSGA-II for biological graph compression

2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

Brown

2020

Epidemic contact tracing examines the movement of infection through a population based upon links in a contact network, and weighted networks represent the potential of transfer of the contagion. Graph compression reduces the size of a network by merging groups of nodes into supernodes. This study considers the use of genetic algorithms to select the nodes to be merged, grouping together highly connected sections of the graphs. Examined is a dataset that is extracted from contacts that occurred during several days of the "Infectious: Stay Away" event. The incorporation of weights, to indicate the strength of interactions between individuals, is an important contribution of this work. The demonstrated outcomes are that by including weighted information on the edges, there is more effective detection of highly interacting subgroups when compared to the unweighted version of graphs. These methods not only compress the networks with a low rate of distortion, but also the identification of supernodes in the networks allows for better targeting of interventions by public health upon individuals in such groups. This is crucial because when one member becomes infected, all members of the group are exposed to the contagion.

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Extracting Information from Weighted Contact Networks via Genetic Algorithms

Rutkowski

2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

Brown

2020

“…The representation and methodology have evolved over the course of a number of previous studies (see, e.g. [9], [32], [16], [20]) and used successfully to address the compression of biological data. A brief description of the representation follows, and we refer the reader to these earlier studies for further details.…”

Section: A Compressionmentioning

confidence: 99%

Evolutionary Graph Compression and Diffusion Methods for City Discovery in Role Playing Games

Brown

Ashlock

2020 IEEE Congress on Evolutionary Computation (CEC)

et al. 2020

Cities, while exciting in their visualization and permitting several layouts, do not take into account the placement of crucial characters which might be part of the narrative. Narrative graphs, a connected graph of all potential and existing relations within a game, can enable an ability to find a Nonplayer Character (NPC) who is likely to live nearby, under the assumption that those who interact most frequently are also close in distance. We examine the use of an evolutionary graph compression method and a method using simulated diffusion to cluster features based on relational information about players to generate relationally intimate groups. This clustering can be used to generate information about the game world and cities to inform PCG as to how the connectivity of these areas is, and should be, arranged. The algorithms are validated as being human competitive.

“…Each chromosome in the population is a sequence of merges which must be local in that both nodes to be merged are within root 22 12 24 71 20 offset 21 94 12 19 7 merges, the first node is chosen randomly and its index is stored in the root. Next, a breadth-first search is performed from that node to find all other nodes within the specified distance; once these are found, one is chosen at random and the offset is set accordingly.…”

Section: B Initial Populationmentioning

confidence: 99%

Compression of Biological Networks using a Genetic Algorithm with Localized Merge

2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

Romualdo

Collins

et al. 2019

Network graphs appear in a number of important biological data problems, recording information relating to protein-protein interactions, gene regulation, transcription regulation and much more. These graphs are of such a significant size that they are impossible for a human to understand. Furthermore, the ever-expanding quantity of such information means that there are storage issues. To help address these issues, it is common for applications to compress nodes to form supernodes of similarly connected components. In previous graph compression studies it was noted that such supernodes often contain points from disparate parts of the graph. This study aims to correct this flaw by only allowing merges to occur within a local neighbourhood rather than across the entire graph. This restriction was found to not only produce more meaningful compressions, but also to reduce the overall distortion created by the compression for two out of three biological networks studied.