Jul 2026· Annual International ACM SIGIR Conference on Research and Development in Information Retrieval· pp. 4727-4731· 0 citations· 26 references
Computer Science
TL;DR
This paper presents a production-ready framework deployed at TikTok, which integrates three core contributions to address the challenges of large-scale A/B tests on social platforms, and reduces interference rates by 68.8%, correcting a biased treatment effect estimate and enables previously undetectable cross-ecosystem measurements.
Abstract
Large-scale A/B tests on social platforms suffer from inherent network interference, violating the Stable Unit Treatment Value Assumption (SUTVA) and distorting measured results. Industrial practices for mitigating network interference face a foundational trade-off. For scalability, they often rely on clustering static graphs, which serve as imperfect proxies for true interference pathways. Conversely, theoretically sound methods remain computationally intractable at production scale. This paper presents a production-ready framework deployed at TikTok, which integrates three core contributions to address these challenges: 1) Learned Interference Graph (LIG): Estimates interference probabilities using dynamic interaction patterns for more context-aware modeling. 2) Scalable Community Partitioning (SCP): A Spark-optimized ParLeiden implementation that performs billion-node graph clustering daily and generalizes effectively across diverse interaction types, achieving a purity score of 0.898 for group chat interactions. 3) Sensitivity-Enhanced Estimation (SEE): A multivariate system leveraging Controlled-experiment Using Pre-Experiment Data (CUPED) to mitigate variance inflation from cluster-based randomization. In live production tests, our framework reduces interference rates by 68.8%, correcting a biased treatment effect estimate from +1.44% to a statistically significant +2.08%. It also enables previously undetectable cross-ecosystem measurements, revealing a +0.2% lift in creator upload volumes driven by user-side treatments.
Graph Similarity Computation (GSC) is a core task in graph analysis. However, current mainstream GNN-based similarity models still suffer from two fundamental bottlenecks. First, constrained by the inherent mechanism of recursive local aggregation, namely the 1-Weisfeiler–Lehman (1-wl) test, these models primarily measure similarity by aligning local structures, while struggling to capture long-range dependencies and overall topological configurations. Second, the simplified treatment of edge features prevents them from fully exploiting fine-grained semantic interactions between nodes. To address these challenges, this paper pro-poses Structure-Aware Collaborative Network (SAC-Net), an end-to-end framework that leverages structural information to unify global contexts with local affinities. Specifically, we design a Dynamic Structural Perception (DSP) backbone to establish a joint evolution paradigm for node, position, and edge features. By treating positional encodings as dynamic states, the model effectively captures long-range dependencies and overall topological configurations to maintain a robust global structural skeleton. Subsequently, this study introduce an Edge-Aware Fusion mechanism that leverages edge features as a bridge to adaptively integrate global and local structural information, thereby effectively addressing the alignment and integration of multi-granularity semantics. Extensive experiments on four real-world datasets demonstrate that SAC-Net effectively integrates global and local information, leading to more accurate graph similarity measurement.
Ling-Han Zeng, Yan-Ling Li, Ming-Xia Bi et al.· Tsinghua Science and Technol...· 0 citations
Bipartite graphs are ubiquitous for modeling complex interactions between two distinct entity types across numerous practical applications such as e-commerce, academic networks, and social systems. Despite significant progress in community search over bipartite graphs, most prior work is limited to static settings and ignores the rich temporal dynamics present in real-world networks. Moreover, existing methods typically adopt edge-centric measures and strict consecutivity constraints, failing to capture higher-order interactions and frequent yet non-consecutive activities. More importantly, they often neglect the crucial community-quality requirements of both internal cohesiveness and external sparsity, failing to identify critical nodes or including many irrelevant nodes. To address these dilemmas, we propose the novel problem of \emph{Wedge Conductance Community Search (WCCS)}, which aims to identify a query-dependent community that is not only structurally and temporally cohesive but also well-separated from the rest of the network over non-consecutive timestamps. We formalize WCCS by generalizing the classical $(\alpha,\beta)$-core to a higher-order $(\alpha,\beta,\tau)$-wedge core, and by proposing a novel temporal wedge conductance metric that explicitly balances internal density and external sparsity. To solve WCCS efficiently, we first develop an online priority-driven filter-and-expand framework with several effective pruning techniques and a powerful geometric slope optimization for rapid temporal wedge conductance calculation. Subsequently, to further improve scalability, we propose an offline compressed index to accelerate search. Finally, comprehensive experiments on seven real-world datasets demonstrate the effectiveness, efficiency, and scalability of our solutions compared to eight competitors.
Long-Long Lin, Wei Chen, Ping-Peng Yuan et al.· 0 citations
A lightweight, system-wide global termination detection algorithm that enables vertices to decide locally when the overall system has converged, and emphasises the need for coordinated halting in distributed centrality computation.
Siamak Abdi, Lucia Cavallaro, G. Di Fatta· arXiv.org· 0 citations
Noise Corruption is introduced, a Noise Corruption-based explanation framework, which perturbs each message through matched-norm random-direction corruption while preserving the expected squared message norm, and NICE, a Noise Corruption-based explanation framework, which learns a Stochastic Restoration Boundary under NC-induced uncertainty, balancing target-prediction restoration against compactness.
Ziluowen Luo, Jun Yin, Ruo-Chen Liu et al.· 0 citations
Approximating Monte Carlo Independent Cascade (MC-IC) influence rankings with a learned surrogate is only meaningful if the target itself is clearly defined. We study this dependency on the Twitch Gamers graph under two IC operationalizations, keeping the graph, labeled-node sample, split, Monte Carlo budget, and evaluation protocol fixed while using regime-aligned feature policies. Under the structural weighted-cascade regime, binary top-k labels are too unstable for the primary target; in this setting, the best raw-attribute GNN, GCN, remains below degree centrality (ρ = 0.808 vs. 0.826). Under the source-community operationalization, degree centrality becomes uninformative (ρ = −0.006), and raw-attribute GraphSAGE outperforms the pre-specified flat linear-regression (LR) baseline (ρ = 0.915 vs. 0.884). Trained surrogates provide sub-second full-graph inference, whereas MC label generation takes hundreds to about two thousand seconds on the frozen labeled subset. The central finding is conditional: on this graph, neighborhood-aware surrogates add value beyond strong structural, flat, and shallow-embedding alternatives when the target construction moves influence signal away from local degree structure. A supplementary 1-hop neighbor-aggregation diagnostic reaches a Spearman correlation comparable to that of standard Graph-SAGE in the source-community regime, suggesting that part of the observed gain may be attributable to local neighborhood aggregation rather than to learned message passing specifically. The source code and dataset are available at https://github.com/qvinhx89/operationalization-matters-influence.
Dinh-Duy Tran, Q. Pham, Q. Tran et al.· International Conference on...· 0 citations
Information diffusion prediction forecasts future participants from an observed cascade prefix, enabling proactive intervention in applications such as viral marketing and misinformation mitigation. Most existing models leverage two data sources: the global social graph (exposure/trust pathways) and cascade-induced interaction relations (interest-driven co-adoption), following a ''learn-then-fuse'' pipeline that encodes both graphs with GNNs and combines them via gated fusion to condition a sequential decoder. However, we find the two views are systematically mismatched: interaction edges are largely disjoint from social links, most social neighbors never co-activate within the same cascade, and the resulting embeddings lie on near-orthogonal manifolds with negligible correspondence. With such mismatch, static fusion is ill-posed: when the views disagree, fusion enforces a compromise and can cause negative interference. We further identify three reliability mechanisms that determine when each view should be trusted: (1) behavioral consensus across views is a high-fidelity signal of influence; (2) social cues are essential in cold-start regimes where interactions are sparse and biased; and (3) social ties dominate early seeding, while interaction patterns govern the late viral stage. Motivated by these, we propose CARD, a context-aware routing framework that replaces static fusion with step-wise evidence arbitration. CARD constructs an expert pool with social and interaction experts, a consensus expert that activates when both views are confirmed to behavioral consensus, and a graph-agnostic prior expert for noisy fallback. A router hard-selects the single most reliable expert at each step, so the decoder receives a targeted signal rather than a blurred mixture. Extensive experiments on four real-world datasets show that CARD achieves state-of-the-art accuracy and stronger robustness.
Zi-Han Feng, Yajun Yang, Rui Wu et al.· Proceedings of the 32nd ACM...· 0 citations
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