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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2401.14375 |
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| _version_ | 1866913224484454400 |
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| author | Tsoukanara, Evangelia Koloniari, Georgia Pitoura, Evaggelia Triantafillou, Peter |
| author_facet | Tsoukanara, Evangelia Koloniari, Georgia Pitoura, Evaggelia Triantafillou, Peter |
| contents | When the focus is on the relationships or interactions between entities, graphs offer an intuitive model for many real-world data. Such graphs are usually large and change over time, thus, requiring models and strategies that explore their evolution. We study the evolution of aggregated graphs and introduce the GraphTempo model that allows temporal and attribute aggregation not only on node level by grouping individual nodes, but on a pattern level as well, where subgraphs are grouped together. Furthermore, We propose an efficient strategy for exploring the evolution of the graph based on identifying time intervals of significant growth, shrinkage or stability. Finally, we evaluate the efficiency and effectiveness of the proposed approach using three real graphs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_14375 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The GraphTempo Framework for Exploring the Evolution of a Graph through Pattern Aggregation Tsoukanara, Evangelia Koloniari, Georgia Pitoura, Evaggelia Triantafillou, Peter Social and Information Networks When the focus is on the relationships or interactions between entities, graphs offer an intuitive model for many real-world data. Such graphs are usually large and change over time, thus, requiring models and strategies that explore their evolution. We study the evolution of aggregated graphs and introduce the GraphTempo model that allows temporal and attribute aggregation not only on node level by grouping individual nodes, but on a pattern level as well, where subgraphs are grouped together. Furthermore, We propose an efficient strategy for exploring the evolution of the graph based on identifying time intervals of significant growth, shrinkage or stability. Finally, we evaluate the efficiency and effectiveness of the proposed approach using three real graphs. |
| title | The GraphTempo Framework for Exploring the Evolution of a Graph through Pattern Aggregation |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2401.14375 |