A temporal graph is an undirected graph where each edge is associated with a timestamp indicating when it occurs. As a fundamental topic in graph analysis, community search (CS) in temporal graphs has received much attention. Existing CS works on temporal graphs typically identify sets of vertices that form a
k
-core within a specific time window (temporal
k
-core). However, they overlook the duration of a temporal community, which is the continues time period that its members remain unchanged. Intuitively, the longer the duration of a temporal community, the higher its stability. Long-duration communities are useful in many areas, such as event detection and network analysis. In this paper, we introduce a novel community model, called temporal durable community (TDC), which is the temporal
k
-core with the longest duration in the temporal graph, and aim to efficiently find the TDC containing a query vertex. To solve this problem, we first propose a novel online algorithm based on binary search. We further develop two index structures that can quickly determine the duration of a given temporal
k
-core, followed by query algorithms. Experiments on ten real large temporal graphs show that our TDC model is effective for finding stable communities, and our index-based query algorithms are up to five orders of magnitude faster than the online algorithm.
Yingli Zhou, Yi-Ge Jiang, Yixiang Fang et al.· Proceedings of the VLDB Endo...· 0 citations
While ByteDance's database infrastructure continues to serve bursty user traffic, emerging large-scale interactive applications generate increasingly write-intensive workloads spanning all stages of the data lifecycle. In production environments, we observe that these workloads are consistently bottlenecked by a common write-path inefficiency rooted in cache-miss-induced remote page fetches. In compute-storage disaggregated architectures, such remote page accesses introduce substantial I/O latency that dominates write performance. To address this bottleneck, we propose Write Operation Pushdown (WOP), a framework that eliminates Fetch-Before-Write (FBW) from the write path by pushing eligible writes to the storage layer for asynchronous execution. WOP leverages the redo-flush path to push down write operations and enhances the storage layer for semantic-aware execution. WOP supports high pushdown capacity with minimal memory footprint through Lightweight Metadata Tracking. WOP preserves correctness and consistency in distributed deployments via carefully designed mechanisms. To prevent read stalls introduced by asynchronous execution, WOP integrates a Fetch-After-Write (FAW) optimization with localized, on-demand replay. Across production workloads, WOP achieves up to 3.33× throughput improvement. On synthetic benchmarks, WOP improves throughput by up to 6.7× and reduces average latency by up to 85.2%. These results show that WOP significantly enhances write efficiency in industrial-scale workloads while preserving correctness in disaggregated deployments.
Hao Wu, Lu-Tong Zhang, Xin-Ying Zheng et al.· Proceedings of the VLDB Endo...· 0 citations
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