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Improvement of PRO-HEAPS Algorithm to Analyze Interaction Changes in Heterogeneous Graph
Bukit T.a, Saptawati P.a
a Bandung Institute of Technology, School of Electrical Engineering and Informatics, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.In this study, a path interaction searching method between two entities was developed on heterogeneous graph data by making improvement for PRO-HEAPS algorithm. The PRO-HEAPS algorithm provides better performance than other path searching methods, but this algorithm has not been able to support searching paths in an evolved graph data. The approach to developing the PRO-HEAPS algorithm in this study was done by modifying the path searching query and adding the preprocessing stage to process evolved graph data. The preprocessing stage is designed by selecting which time snapshots meet the query requirements, then running the PRO-HEAPS algorithm on each time snapshot of the evolved graph. In this study, an experiment was conducted to test the results of PRO-HEAPS improvement on evolved graph data. Implementation of the improvement of the PRO-HEAPS algorithm is done by developing desktop-based applications using C++ programming language. The data testing used in this experiment are synthesis data and DBLP data. Synthesis data is based on previous research by Liang et al. in 2018. While in the DBLP data, 6-time snapshots were selected from 1990 – 1995 to perform experiments. From this study, it can be concluded that the PRO-HEAPS algorithm was able to be improved to perform path searching on evolved graph data. The improvement is done by modifying the path searching query using the temporal query concept. The PRO-HEAPS algorithm was also developed by adding pre-processing stages to process evolved graphs. This pre-processing stage can select and save the snapshot time of graph data. Based on the experimental results, the application of the PRO-HEAPS development was able to maintain the ability of the functionality of the original PRO-HEAPS algorithm and be able to search paths for evolved graph data.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Data testing,Graph data,Heterogeneous graph,Pre-processing,Searching methods,Support searching,Temporal queries[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]graph query,heterogeneous graph,path searching[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICoDSE48700.2019.9092735[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]