Human-Powered Data Cleaning for Probabilistic Reachability Queries on Uncertain Graphs

Many studies have been conducted on seeking an efficient solution for graph similarity search over certain (deterministic) graphs due to its wide application in many fields, including bioinformatics, social network analysis, and Resource Description Framework data management. All prior work assumes that the underlying data is deterministic. However, in reality, graphs are often noisy and uncertain due to various factors, such as errors in data extraction, inconsistencies in data integration, and for privacy-preserving purposes. Therefore, in this paper, we study similarity graph containment search on large uncertain graph databases. Similarity graph containment search consists of subgraph similarity search and supergraph similarity search. Different from previous works assuming that edges in an uncertain graph are independent of each other, we study uncertain graphs where edges' occurrences are correlated. We formally prove that subgraph or supergraph similarity search over uncertain graphs is $$\#$&#P-hard; thus, we employ a filter-and-verify framework to speed up these two queries. For the subgraph similarity query, in the filtering phase, we develop tight lower and upper bounds of subgraph similarity probability based on a probabilistic matrix index (PMI). PMI is composed of discriminative subgraph features associated with tight lower and upper bounds of subgraph isomorphism probability. Based on PMI, we can filter out a large number of uncertain graphs and maximize the pruning capability. During the verification phase, we develop an efficient sampling algorithm to validate the remaining candidates. For the supergraph similarity query, in the filtering phase, we propose two pruning algorithms, one lightweight and the other strong, based on maximal common subgraphs of query graph and data graph. We run the two pruning algorithms against a probabilistic index that consists of powerful graph features. In the verification, we design an approximate algorithm based on the Horvitz---Thompson estimator to fast validate the remaining candidates. The efficiencies of our proposed solutions to the subgraph and supergraph similarity search have been verified through extensive experiments on real uncertain graph datasets.

The k-truss of a graph is the largest edge-induced subgraph such that every edge is contained in at least k triangles within the subgraph, where a triangle is a cycle consisting of three vertices. As a new notion of cohesive subgraphs, truss has recently attracted a lot of research attentions in the database and data mining fields. At the same time, uncertainty is an intrinsic property of massive graph data, and truss decomposition (i.e., finding all k-trusses of a graph) has become a key primitive on uncertain graphs. In this paper, we study the truss decomposition problem on uncertain graphs, that is, finding all highly probable k-trusses of an uncertain graph. We first give an formal statement of the truss decomposition problem on uncertain graphs. Then, we prove that the truss decomposition of an uncertain graph attains two elegant properties, namely uniqueness and hierarchy. We show that the truss decomposition of an uncertain graph can be found in $$O(m^{1.5}Q)$$O(m1.5Q) time by proposing an in-memory algorithm called $$\mathtt {TD_{mem}}$$TDmem, where m is the number of edges of the uncertain graph, and Q is at most the maximum number of common neighbors of the endpoints of an edge. When an uncertain graph is too large to fit into main memory, we propose an external-memory algorithm $$\mathtt {TD_{I/O}}$$TDI/O to find the truss decomposition of the uncertain graph. Extensive experiments have been carried out to evaluate the practical performance of the proposed algorithms. The experimental results verify that both $$\mathtt {TD_{mem}}$$TDmem and $$\mathtt {TD_{I/O}}$$TDI/O are efficient when an uncertain graph is small enough to fit into main memory, and that $$\mathtt {TD_{I/O}}$$TDI/O is much faster than $$\mathtt {TD_{mem}}$$TDmem when the graph is too large to fit into main memory.

Community search (CS) on graphs returns the largest densely connected vertex subset containing a query vertex, namely k-community, where every vertex's degree in the induced subgraph is not less than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> . It has significant influence in many real-life applications including event organization and friend recommendation. Many complex networks such as social networks and protein-protein interaction (PPI) networks are often modeled as uncertain graphs. In this paper, we identify and study the problem of reliable community search on uncertain graphs (UCS for short). Given an uncertain graph, a query vertex <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> , a positive integer <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> and a probability threshold θ, the reliable community, viz., (k, θ) -community, of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> is the largest vertex subset, so that the probability of every vertex to be in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> 's k-community is not less than θ. We prove that it is a NP-hard problem. We propose two novel pruning strategies to reduce the candidate set to a much smaller size. We develop an efficient index, namely CD-index, with which the pruning process can be done in optimal time. We also present efficient sampling algorithms on top of stratified sampling and lazy sampling to accelerate the search under accuracy guarantees. Extensive experiments using four real-world datasets demonstrate the superior performance of proposed algorithms to the state-of-the-art approaches.

Graphs play an important role in modern world, due to their widespread use for modeling, representing and organizing linked data. Taking into consideration that most of the “killer” applications require a graph-based representation (e.g., the Web, social network management, protein-protein interaction networks), efficient query processing and analysis techniques are required, not only because these graphs are massive but also because the operations that must be supported are complex, requiring significant computational resources. In many cases, each graph edge e is annotated by a probability value p(e), expressing its existential uncertainty. This means that with probability p(e) the edge will be present in the graph and with probability \(1-p(e)\) the edge will be absent. This gives rise to the concept of probabilistic graphs (also known as uncertain graphs). Formally, a probabilistic graph \(\mathcal{G}\) is a triplet (V, E, p) where V is the set of nodes, E is the set of edges and \(p: E \rightarrow (0,1]\). The main challenge posed by this formulation is that problems that are relatively easy to solve in exact graphs become very difficult (or even intractable) in probabilistic graphs. In this paper, we perform an overview of the algorithmic techniques proposed in the literature for uncertain graph analysis. In particular, we center our focus on the following graph mining tasks: clustering, maximal cliques, k-nearest neighbors and core decomposition. We conclude the paper with a short discussion related to distributed mining of uncertain graphs which is expected to achieve significant performance improvements.

Extending graph models to incorporate uncertainty is important for many applications, including disease transmission networks, where edges may have a disease transmission probability associated with them, and social networks, where nodes may have an existence probability associated with them. Analysts need tools that support analysis and comparison of these uncertain graphs. To this end, we have developed a prototype SQL-like graph query language with emphasis on operators for uncertain graph comparison. In order to facilitate adding new operators and to enable developers to use existing operators as building blocks for more complex ones, we have implemented a query engine with an extensible system architecture. The utility of our query language and operators in analyzing uncertain graph data is illustrated using two real world data sets: a dolphin observation network and a citation network. Our approach serves as an example for developing simple query languages that enables users to write their own ad-hoc uncertain graph comparison queries without extensive programming knowledge.

The problem of graph classification has attracted much attention in recent years. The existing work on graph classification has only dealt with precise and deterministic graph objects. However, the linkages between nodes in many real-world applications are inherently uncertain. In this paper, we focus on classification of graph objects with uncertainty. The method we propose can be divided into three steps: Firstly, we put forward a framework for classifying uncertain graph objects. Secondly, we extend the traditional algorithm used in the process of extracting frequent subgraphs to handle uncertain graph data. Thirdly, based on Extreme Learning Machine (ELM) with fast learning speed, a classifier is constructed. Extensive experiments on uncertain graph objects show that our method can produce better efficiency and effectiveness compared with other methods.

Among uncertain graph queries, reachability, i.e., the probability that one vertex is reachable from another, is likely the most fundamental one. Although this problem has been studied within the field of network reliability, solutions are implemented on a single computer and can only handle small graphs. However, as the size of graph applications continually increases, the corresponding graph data can no longer fit within a single computer's memory and must therefore be distributed across several machines. Furthermore, the computation of probabilistic reachability queries is #P-complete making it very expensive even on small graphs. In this paper, we develop an efficient distributed strategy, called DistR , to solve the problem of reachability query over large uncertain graphs. Specifically, we perform the task in two steps: distributed graph reduction and distributed consolidation . In the distributed graph reduction step, we find all of the maximal subgraphs of the original graph, whose reachability probabilities can be calculated in polynomial time, compute them and reduce the graph accordingly. After this step, only a small graph remains. In the distributed consolidation step, we transform the problem into a relational join process and provide an approximate answer to the #P-complete reachability query. Extensive experimental studies show that our distributed approach is efficient in terms of both computational and communication costs, and has high accuracy.

Knowledge graphs have emerged as a transformative tool in enhancing personalized recommendation systems. By integrating diverse datasets into a structured semantic network, knowledge graphs offer a holistic view of relationships and entities that can significantly improve the relevance and accuracy of recommendations. Unlike traditional recommendation algorithms that rely primarily on user behaviour and item similarity, knowledge graphs leverage contextual information and complex interconnections among entities to deliver more nuanced and context-aware suggestions. This abstract explores the pivotal role of knowledge graphs in advancing personalized recommendation systems, focusing on their ability to capture intricate relationships between users, items, and attributes. By mapping out these relationships, knowledge graphs facilitate a deeper understanding of user preferences and item characteristics, enabling the generation of more tailored and precise recommendations. Additionally, the incorporation of external knowledge sources into the graph can further enrich the recommendation process, leading to enhanced user satisfaction and engagement. The paper reviews various methodologies for integrating knowledge graphs into recommendation systems, including graph-based algorithms and machine learning techniques. It also examines real-world applications and case studies where knowledge graphs have demonstrated substantial improvements in recommendation quality. Ultimately, the utilization of knowledge graphs represents a significant leap forward in personalizing user experiences, offering a promising avenue for future research and development in the field of recommendation systems.

With the exponential growth of graph‐structured data, single‐machine efficient analysis has become increasingly impractical, making high‐performance distributed graph computing systems indispensable. The efficacy of these systems hinges critically on high‐quality graph partitioning. The edges of many graphs stemmed from real applications are uncertain, but many existing graph partitioning algorithms are only for deterministic graphs without considering uncertainty. This paper presents a novel partitioning algorithm, PAUG (Partitioning Algorithm for Uncertain Graphs), tailored for uncertain graphs. First, it formalizes the partitioning problem as an optimization task to minimize the cut‐edge ratio while balancing load. Second, it introduces probabilistic similarity to quantify vertex relationships under uncertainty. Finally, it details the PAUG algorithm which consists of initial partition phase and score‐function‐guided refinement strategy. Experimental results shows that PAUG achieves an average 23.2% reduction in cut‐edge ratio and a 26.2% improvement in load balance over state‐of‐the‐art algorithms.

Geospatial data is ubiquitous in today’s world, playing a crucial role in various aspects of modern life. As businesses increasingly establish and expand their online presence, they leverage location-based insights to better understand customer behavior and optimize services. Simultaneously, the proliferation of GPS-enabled devices enables users to continuously share their locations and activities online, contributing to an ever-growing pool of geospatial information. Social networks and web services, in particular, heavily rely on this data, integrating it into their platforms to provide more personalized and relevant experiences. This surge in geospatial data generation necessitates the development of novel algorithms to mine meaningful patterns and enhancing user experiences by delivering accurate information at the right place and time. This dissertation presents a pipeline of novel algorithms, spanning from the integration of geospatial data, to its effective application in real-world use cases. Specifically it introduces (1) an algorithm for deduplicating and consolidating geospatial data from multiple sources, a key step in creating a comprehensive geospatial database; it studies (2) the problem of Geospatial Knowledge Graph (GeoKG) construction, and presents an algorithm to automatically mine fine-grained spatial relationships between entities from a database, within an open-world setting; it presents (3) a framework for self-supervised pre-training of geospatial foundation models, capable of generating task-agnostic representations that are readily applicable to a wide range of downstream tasks. Finally, (4) this thesis explores the fine-tuning of pre-trained large language models (LLMs) on city-specific data, to build urban virtual assistants, capable of delivering accurate geospatial recommendations in a conversational manner while minimizing hallucinations. The study on geospatial Entity Resolution (ER) proposes a state-of-the-art approach to link records from different sources, that refer to the same real-world geospatial entity. This is a crucial step for deduplicating merged data. The algorithm, called Geo-ER, leverages textual, spatial, and contextual information, making a step forward towards automatic geospatial data integration. A novel neighborhood embedding component is designed, using Graph Attention Networks, to produce a more contextualized representation of an entity. Extensive experiments, on eight real-world datasets, are performed to demonstrate the advantages of Geo-ER compared to existing state-of-the-art solution for geospatial and traditional ER. Cross-city validation experiments showcase the algorithm's robustness in scenarios where training data for a specific city is unavailable. Subsequently, this thesis presents GTMiner, a novel framework for automatically constructing a Geospatial Knowledge Graph (GeoKG) from tabular data, derived from the merging and consolidation of multiple data sources. The architecture for geospatial relation prediction is an open-world Knowledge Graph Completion solution, consisting of a pre-trained language model, a geospatial encoder, and a Geo-Textual interaction component, which jointly model the textual and geospatial characteristics of an entity. GTMiner further includes a refinement module, specifically designed to improve both coverage and correctness of a GeoKG. The study further introduces four real-world datasets, collected from publicly available sources, and carries out extensive experiments to evaluate the performance of each of the system’s components and perform ablation studies. A study on general-purpose representation learning in the geospatial domain introduces CityFM, a new framework that leverages mutual information maximization and incorporates Nodes, Ways, and Relations into its learning frame-work to train geospatial Pre-trained Foundation Models (PFMs) for a selected area of interest. CityFM-trained models are task-agnostic and multimodal, capable of integrating an entity’s textual, visual, and spatial attributes. The study conducts experiments on road, building, and region-level downstream tasks to demonstrate the effectiveness of the embeddings produced by the foundation models in real-world applications, compared to task-specific algorithms. The last study in this dissertation presents LAMP, a virtual assistant for urban applications. It introduces a novel strategy to generate synthetic conversational data about geospatial entities, to fine-tune a pre-trained LLM. The generated data enables the language model to learn and memorize thousands of places within a city, while also equipping it with spatial (proximity) awareness specific to the urban area of interest.

BackgroundIn recent years, large language models (LLMs) have shown promise in various domains, notably in biomedical sciences. However, their real-world application is often limited by issues like erroneous outputs and hallucinatory responses.ResultsWe developed the knowledge graph–based thought (KGT) framework, an innovative solution that integrates LLMs with knowledge graphs (KGs) to improve their initial responses by utilizing verifiable information from KGs, thus significantly reducing factual errors in reasoning. The KGT framework demonstrates strong adaptability and performs well across various open-source LLMs. Notably, KGT can facilitate the discovery of new uses for existing drugs through potential drug–cancer associations and can assist in predicting resistance by analyzing relevant biomarkers and genetic mechanisms. To evaluate the knowledge graph question answering task within biomedicine, we utilize a pan-cancer knowledge graph to develop a pan-cancer question answering benchmark, named pan-cancer question answering.ConclusionsThe KGT framework substantially improves the accuracy and utility of LLMs in the biomedical field. This study serves as a proof of concept, demonstrating its exceptional performance in biomedical question answering.

To address the sparsity and cold-start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve the performance of recommendation. In this article, we consider the knowledge graph (KG) as the source of side information. To address the limitations of existing embedding-based and path-based methods for KG-aware recommendation, we propose RippleNet , an end-to-end framework that naturally incorporates the KG into recommender systems. RippleNet has two versions: (1) The outward propagation version, which is analogous to the actual ripples on water, stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user’s potential interests along links in the KG. The multiple “ripples” activated by a user’s historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item. (2) The inward aggregation version aggregates and incorporates the neighborhood information biasedly when computing the representation of a given entity. The neighborhood can be extended to multiple hops away to model high-order proximity and capture users’ long-distance interests. In addition, we intuitively demonstrate how a KG assists with recommender systems in RippleNet, and we also find that RippleNet provides a new perspective of explainability for the recommended results in terms of the KG. Through extensive experiments on real-world datasets, we demonstrate that both versions of RippleNet achieve substantial gains in a variety of scenarios, including movie, book, and news recommendations, over several state-of-the-art baselines.

PurposeThere is an increasing interest in the use of knowledge graphs to represent real-world knowledge and a common need to manage imprecise knowledge in many real-world applications. This paper aims to study approaches to solve flexible queries over knowledge graphs.Design/methodology/approachBy introducing fuzzy logic in the query answering process, the authors are able to obtain a novel algorithm to solve flexible queries over knowledge graphs. This approach is implemented in the FUzzy Knowledge Graphs system, a software tool with an intuitive user-graphical interface.FindingsThis approach makes it possible to reuse semantic web standards (RDF, SPARQL and OWL 2) and builds a fuzzy layer on top of them. The application to a use case shows that the system can aggregate information in different ways by selecting different fusion operators and adapting to different user needs.Originality/valueThis approach is more general than similar previous works in the literature and provides a specific way to represent the flexible restrictions (using fuzzy OWL 2 datatypes).

Knowledge graphs (KGs) are widely used in many real-world applications, such as information retrieval, question answering system, and personal recommendation. However, most KGs are suffering from the incompleteness problem. To deal with the task of link prediction, previous knowledge graph embedding methods require numerous reference instances for each relation. It is worth noting that most relations in KGs have only a few reference instances available. Existing works for few-shot link prediction evaluate the authenticity of triplets from a single relation perspective. In this paper, we propose Hybrid Matching Network (HMNet) for few-shot link prediction, evaluating triplets from entity and relation two perspectives. At the entity-aware matching network, HMNet uses attentive inductive embedding layer to aggregate entity features and relation-aware topology, and then provides entity-aware score to implement first perspective evaluation. At the relation-aware matching network, HMNet integrates feature attention mechanism to implement relation perspective evaluation. Experiments on two public datasets indicate that HMNet achieves promising performance in few-shot link prediction.

Few-shot link prediction for temporal knowledge graphs based on time-aware translation and attention mechanism