Social networks represent complex ecosystems where the interactions between users or groups play a pivotal role in information dissemination, opinion formation, and social interactions.Effectively harnessing event sequence data within social networks to unearth interactions among users or groups has persistently posed a challenging frontier within the realm of point processes.Current deep point process models face inherent limitations within the context of social networks, constraining both their interpretability and expressive power.These models encounter challenges in capturing interactions among users or groups and often rely on parameterized extrapolation methods when modeling intensity over non-event intervals, limiting their capacity to capture complex intensity patterns beyond observed events.To address these challenges, this study proposes modifications to Transformer Hawkes processes (THP), leading to the development of interpretable Transformer Hawkes processes (ITHP).ITHP inherits the strengths of THP while aligning with statistical nonlinear Hawkes processes, thereby enhancing its interpretability and providing valuable insights into interactions between users or groups.Additionally, ITHP enhances the flexibility of the intensity function over non-event intervals, making it better suited to capture complex event propagation patterns in social networks.Experimental results, both on synthetic and real data, demonstrate the effectiveness of ITHP in overcoming the identified limitations.Moreover, they highlight ITHP's applicability in the context of exploring the complex impact

Pipe failure prediction in the water industry aims to prioritize the pipes that are at high risk of failure for proactive maintenance. However, existing statistical or machine learning models that rely on historical failures and asset attributes can hardly leverage the structure information of pipe networks. In this work, we develop a failure prediction framework for pipe networks by jointly considering the pipes' features, the network structure, the geographical neighboring effect, and the temporal failure series. We apply a multi-hop Graph Neural Network (GNN) to failure prediction. We propose a method of constructing a geographical graph structure depending on not only the physical connections but also geographical distances between pipes. To differentiate the pipes with diverse properties, we employ an attention mechanism in the neighborhood aggregation process of each GNN layer. Also, residual connections and layer-wise aggregation are used to avoid the over-smoothing issue in deep GNNs. The historical failures exhibit a strong temporal pattern. Inspired by point process, we develop a module to learn the pipes' evolutionary effect and the time-decayed excitement of historical failures on the current state of the pipe. The proposed framework is evaluated on two real-world large-scale pipe networks. It outperforms the existing statistical, machine learning, and state-of-the-art GNN baselines. Our framework provides the water utility with core data-driven support for proactive maintenance including regular pipe inspection, pipe renewal planning, and sensor system deployment. It can be extended to other infrastructure networks in the future.

Monitoring drinking water quality in the entire delivery network, mainly indicated by total chlorine (TC), is a critical component of overall water supply management. However, it is extremely difficult to collect sufficient TC data from the network at customer sites, which makes it sparse for comprehensive modelling. This paper details an approach that provides TC prediction within the entire Woronora delivery network in Sydney in the next 24 hours. First, the hydraulic system is employed to capture the topology of the delivery network, so that the water travel time can be estimated using predicted water demand. The travel time links the upstream (reservoir) data to the downstream (resident) data. Then, a two-step strategy is proposed as a semi-parametric method to determine the crucial factors and build Bayesian model for TC decay to predict TC with the travel time. Lastly, the uncertainties of both data and the model are analysed to define the boundaries of prediction for better decision making. Several operational stages are involved when the approach is being deployed, including prediction interpretation, interactive tool development for water quality mapping and visualisation, and proactive optimisation. This has established a successful initiative to improve the overall water supply management for the entire Woronora delivery network.

Failure event prediction is becoming increasingly important in wide applications, such as the planning of proactive maintenance, the active investment management, and disease surveillance. To address the issue, the hazard function in survival analysis has been employed to describe the pattern of failures. Different from traditional survival analysis, this paper discovers how to apply recurrent neural network (RNN) to the long-term hazard function prediction. The proposed Long-Term RNN (LT-RNN) is able to leverage the precedent information shared by other entities, leading to more reliable long-term predictions. Specifically, our method allows a black-box treatment for modelling the hazard function which is often a pre-defined parametric form in typical survival analysis. The key idea of our approach is to model the hazard function as a nonparameteric function of the history. The same precedent information from other entities is embedded to a stitched vector for LT-RNN to automatically learn a representation of the long-term hazard function. We apply our model to the proactive maintenance problem using a large dataset from a water utility in Australia.

Water pipe failures can cause significant economic and social costs, hence have become the primary challenge to water utilities. In this paper, we propose a Bayesian nonparametric approach, namely the Dirichlet process mixture of hierarchical beta process model, for water pipe failure prediction. It can select high-risk pipes for physical condition assessment, thereby preventing disastrous failures proactively.