Cold-start rating prediction is a fundamental problem in recommender systems that has been extensively studied. Many methods have been proposed that exploit explicit relations among existing data, such as collaborative filtering, social recommendations and heterogeneous information network, to alleviate the data insufficiency issue for cold-start users and items. However, the explicit relations constructed based on data between different entities may be unreliable and irrelevant, which limits the performance ceiling of a specific recommendation task. Motivated by this, in this paper, we propose a flexible framework dubbed heterogeneous interaction rating network (HIRE). HIRE does not solely rely on pre-defined interaction patterns or a manually constructed heterogeneous information network. Instead, we devise a Heterogeneous Interaction Module (HIM) to jointly model heterogeneous interactions and directly infer the important interactions via the observed data. In the experiments, we evaluate our framework under 3 cold-start settings on 3 real-world datasets. The experimental results show that HIRE outperforms other baselines by a large margin. Furthermore, we visualize the inferred interactions of HIRE to reveal the intuition behind our framework.

Attributed community search (ACS) aims to identify subgraphs satisfying both structure cohesiveness and attribute homogeneity in attributed graphs, for a given query that contains query nodes and query attributes. Previously, algorithmic approaches deal with ACS in a two-stage paradigm, which suffer from structural inflexibility and attribute irrelevance. To overcome this problem, recently, learning-based approaches have been proposed to learn both structures and attributes simultaneously as a one-stage paradigm. However, these approaches train a transductive model which assumes the graph to infer unseen queries is as same as the graph used for training. That limits the generalization and adaptation of these approaches to different heterogeneous graphs. In this paper, we propose a new framework, Inductive Attributed Community Search, IACS , by inductive learning, which can be used to infer new queries for different communities/graphs. Specifically, IACS employs an encoder-decoder neural architecture to handle an ACS task at a time, where a task consists of a graph with only a few queries and corresponding ground-truth. We design a three-phase workflow, "training-adaptation-inference", which learns a shared model to absorb and induce prior effective common knowledge about ACS across different tasks. And the shared model can swiftly adapt to a new task with small number of ground-truth. We conduct substantial experiments in 7 real-world datasets to verify the effectiveness of IACS for CS/ACS. Our approach IACS achieves 28.97% and 25.60% improvements in F1-score on average in CS and ACS, respectively.

Community Search (CS) is one of the fundamental graph analysis tasks, which is a building block of various real applications. Given any query nodes, CS aims to find cohesive subgraphs that query nodes belong to. Recently, a large number of CS algorithms are designed. These algorithms adopt predefined subgraph patterns to model the communities, which cannot find ground-truth communities that do not have such pre-defined patterns in real-world graphs. Thereby, machine learning (ML) and deep learning (DL) based approaches are proposed to capture flexible community structures by learning from ground-truth communities in a data-driven fashion. These approaches rely on sufficient training data to provide enough generalization for ML models, however, the ground-truth cannot be comprehensively collected beforehand.In this paper, we study ML/DL-based approaches for CS, under the circumstance of small training data. Instead of directly fitting the small data, we extract prior knowledge which is shared across multiple CS tasks via learning a meta model. Each CS task is a graph with several queries that possess corresponding partial ground-truth. The meta model can be swiftly adapted to a task to be predicted by feeding a few task-specific training data. We find that trivially applying multiple classical meta-learning algorithms to CS suffers from problems regarding prediction effectiveness, generalization capability and efficiency. To address such problems, we propose a novel meta-learning based framework, Conditional Graph Neural Process (CGNP), to fulfill the prior extraction and adaptation procedure. A meta CGNP model is a task-common node embedding function for clustering, learned by metric-based graph learning, which fully exploits the characteristics of CS. We compare CGNP with CS algorithms and ML baselines on real graphs with ground-truth communities. Our experiments verify that CGNP outperforms the other native graph algorithms and ML/DL baselines 0.33 and 0.26 on F1 score by average.