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Towards a "Swiss Army Knife" for Scalable User-Defined Temporal $(k,mathcal{X})$-Core Analysis | | Ming Zhong
; Junyong Yang
; Yuanyuan Zhu
; Tieyun Qian
; Mengchi Liu
; Jeffrey Xu Yu
; | | Date: |
1 Sep 2023 | | Abstract: | Querying cohesive subgraphs on temporal graphs (e.g., social network, finance
network, etc.) with various conditions has attracted intensive research
interests recently. In this paper, we study a novel Temporal
$(k,mathcal{X})$-Core Query (TXCQ) that extends a fundamental Temporal
$k$-Core Query (TCQ) proposed in our conference paper by optimizing or
constraining an arbitrary metric $mathcal{X}$ of $k$-core, such as size,
engagement, interaction frequency, time span, burstiness, periodicity, etc. Our
objective is to address specific TXCQ instances with conditions on different
$mathcal{X}$ in a unified algorithm framework that guarantees scalability. For
that, this journal paper proposes a taxonomy of measurement
$mathcal{X}(cdot)$ and achieve our objective using a two-phase framework
while $mathcal{X}(cdot)$ is time-insensitive or time-monotonic. Specifically,
Phase 1 still leverages the query processing algorithm of TCQ to induce all
distinct $k$-cores during a given time range, and meanwhile locates the "time
zones" in which the cores emerge. Then, Phase 2 conducts fast local search and
$mathcal{X}$ evaluation in each time zone with respect to the time
insensitivity or monotonicity of $mathcal{X}(cdot)$. By revealing two
insightful concepts named tightest time interval and loosest time interval that
bound time zones, the redundant core induction and unnecessary $mathcal{X}$
evaluation in a zone can be reduced dramatically. Our experimental results
demonstrate that TXCQ can be addressed as efficiently as TCQ, which achieves
the latest state-of-the-art performance, by using a general algorithm framework
that leaves $mathcal{X}(cdot)$ as a user-defined function. | | Source: | arXiv, 2309.00361 | | Services: | Forum | Review | PDF | Favorites | |
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