Date of Award
Master of Science
Inverted index has been extensively used in Information retrieval systems for document relatedqueries. We consider the generic case of graph storage using Inverted Index and compressing themusing this format. Graph compression by reordering has been done using traversal and clusteringbased techniques. In generic methods, the graph is reordered to arrive at new identifiers for thevertices. The reordered graph is then encoded using an encoding format. The reordering to achievemaximal compression is a well known NP complete problem, the Optimal Linear Arrangement.
Our work focuses on the inverted index format, where each node has its corresponding list ofneighbours. We propose a heuristic based graph reordering, using the property that the cost ofeach vertex is bound by its neighbour with largest vertex id. Consider, two vertices x and y withedges a and b respectively. If x>y and a>b, then cost of graph would come down, if the vertex idof x and y are interchanged. Further, experiments shows that using this heuristic helps in achievingcompression rates on par with distributed methods but with reduced utilization of computationresources
Rajarathinam, Pavithra, "Graph compression using heuristic-based reordering" (2018). Graduate Theses and Dissertations. 16659.