Knowledge discovery from unstructured data, including business documents, web content, and news articles, has been a key AI challenge for the financial services industry. Comprehending these corpora and discovering knowledge from them, which could be textual, tabular, or graphic, are the cornerstone of supporting business decisions in the financial services domain, where information retrieval and content analysis techniques are of fundamental importance. We propose a workshop on knowledge discovery from unstructured data in financial services at SIGIR 2023 to highlight the current and emerging opportunities, invite original research, and prompt success sharing between researchers.

We construct a network of 1.6 million nodes from banking transactions of users of Rabobank. We assign two weights on each edge, which are the aggregate transferred amount and the total number of transactions between the users from the year 2010 to 2020. We present a detailed analysis of the unweighted and both weighted networks by examining their degree, strength, and weight distributions, as well as the topological assortativity and weighted assortativity, clustering, and weighted clustering, together with correlations between these quantities. We further study the meso-scale properties of the networks and compare them to a randomized reference system. This will be the first publicly shared dataset of intra-bank transactions, and this work highlights the unique characteristics of banking transaction networks with other scale-free networks.

Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection. Existing approaches have difficulties with three major issues: sparsity and nonlinearity capturing, residual modeling, and network smoothing. We propose Residual Graph Convolutional Network (ResGCN), an attention-based deep residual modeling approach that can tackle these issues: modeling the attributed networks with GCN allows to capture the sparsity and nonlinearity, utilizing a deep neural network allows direct residual learning from the input, and a residual-based attention mechanism reduces the adverse effect from anomalous nodes and prevents over-smoothing. Extensive experiments on several real-world attributed networks demonstrate the effectiveness of ResGCN in detecting anomalies.

Abstract Collective social media provides a vast amount of geo-tagged social posts, which contain various records on spatio-temporal behavior. Modeling spatio-temporal behavior on collective social media is an important task for applications like tourism recommendation, location prediction and urban planning. Properly accomplishing this task requires a model that allows for diverse behavioral patterns on each of the three aspects: spatial location, time, and text. In this paper, we address the following question: how to find representative subgroups of social posts, for which the spatio-temporal behavioral patterns are substantially different from the behavioral patterns in the whole dataset? Selection and evaluation are the two challenging problems for finding the exceptional subgroups. To address these problems, we propose BNPM: a Bayesian non-parametric model, to model spatio-temporal behavior and infer the exceptionality of social posts in subgroups. By training BNPM on a large amount of randomly sampled subgroups, we can get the global distribution of behavioral patterns. For each given subgroup of social posts, its posterior distribution can be inferred by BNPM. By comparing the posterior distribution with the global distribution, we can quantify the exceptionality of each given subgroup. The exceptionality scores are used to guide the search process within the exceptional model mining framework to automatically discover the exceptional subgroups. Various experiments are conducted to evaluate the effectiveness and efficiency of our method. On four real-world datasets our method discovers subgroups coinciding with events, subgroups distinguishing professionals from tourists, and subgroups whose consistent exceptionality can only be truly appreciated by combining exceptional spatio-temporal and exceptional textual behavior.

Abstract Network embedding (NE) is playing a principal role in network mining, due to its ability to map nodes into efficient low-dimensional embedding vectors. However, two major limitations exist in state-of-the-art NE methods: role preservation and uncertainty modeling. Almost all previous methods represent a node into a point in space and focus on local structural information, i.e., neighborhood information. However, neighborhood information does not capture global structural information and point vector representation fails in modeling the uncertainty of node representations. In this paper, we propose a new NE framework, struc2gauss, which learns node representations in the space of Gaussian distributions and performs network embedding based on global structural information. struc2gauss first employs a given node similarity metric to measure the global structural information, then generates structural context for nodes and finally learns node representations via Gaussian embedding. Different structural similarity measures of networks and energy functions of Gaussian embedding are investigated. Experiments conducted on real-world networks demonstrate that struc2gauss effectively captures global structural information while state-of-the-art network embedding methods fail to, outperforms other methods on the structure-based clustering and classification task and provides more information on uncertainties of node representations.

An increasing number of networks are becoming large-scale and continuously growing in nature, such that clustering on them in their entirety could be intractable. A feasible way to overcome this problem is to sample a representative subgraph and exploit its clustering structure (namely, sample clustering process). However, there are two issues that we should address in current studies. One underlying question is how to evaluate the clustering quality of the entire sample clustering process. Another non-trivial issue is that multiple ground-truths exist in networks, thus evaluating the clustering results in such scenario is also a challenging task. In this paper, first we utilize the set-matching methodology to quantitatively evaluate how differently the clusters of the sampled counterpart correspond to the ground-truth(s) in the original graph, and propose several new quality metrics to capture the differences of clustering structure in various aspects. Second, we put forward an evaluation framework for the general problems of evaluating the clustering quality on graph samples. Extensive experiments on various synthetic and real-world graphs demonstrate that our new quality metrics are more accurate and insightful for the sample clustering evaluation than conventional metrics (e.g., NMI). Thus the evaluation framework is effective and practical to assess the clustering quality of the sample clustering process on massive graphs.

Role discovery and community detection in networks are two essential tasks in network analytics where the role denotes the global structural patterns of nodes in networks and the community represents the local connections of nodes in networks. Previous studies viewed these two tasks orthogonally and solved them independently while the relation between them has been totally neglected. However, it is intuitive that roles and communities in a network are correlated and complementary to each other. In this paper, we propose a novel model for simultaneous roles and communities detection (REACT) in networks. REACT uses non-negative matrix tri-factorization (NMTF) to detect roles and communities and utilizes L2,1 norm as the regularization to capture the diversity relation between roles and communities. The proposed model has several advantages comparing with other existing methods: (1) it incorporates the diversity relation between roles and communities to detect them simultaneously using a unified model, and (2) it provides extra information about the interaction patterns between roles and between communities using NMTF. To analyze the performance of REACT, we conduct experiments on several real-world SNs from different domains. By comparing with state-of-the-art community detection and role discovery methods, the obtained results demonstrate REACT performs best for both role and community detection tasks. Moreover, our model provides a better interpretation for the interaction patterns between communities and between roles.

Role/block discovery is an essential task in network analytics so it has attracted significant attention recently. Previous studies on role discovery either relied on first or second-order structural information to group nodes but neglected the higher-order information or required the number of roles/blocks as the input which may be unknown in practice. To overcome these limitations, in this paper we propose a novel generative model, infinite motif stochastic blockmodel (IMM), for role discovery in networks. IMM takes advantage of high-order motifs in the generative process and it is a nonparametric Bayesian model which can automatically infer the number of roles. To validate the effectiveness of IMM, we conduct experiments on synthetic and real-world networks. The obtained results demonstrate IMM outperforms other blockmodels in role discovery task.

Due to the growing presence of large-scale and streaming graphs such as social networks, graph sampling and clustering play an important role in many real-world applications. One key aspect of graph clustering is the evaluation of cluster quality. However, little attention has been paid to evaluation measures for clustering quality on samples of graphs. As first steps towards appropriate evaluation of clustering methods on sampled graphs, in this work we present two novel evaluation measures for graph clustering called δ-precision and δ-recall. These measures effectively reflect the match quality of the clusters in the sampled graph with respect to the ground-truth clusters in the original graph. We show in extensive experiments on various benchmarks that our proposed metrics are practical and effective for graph clustering evaluation.

Query-oriented relevance, information richness and novelty are important requirements in query-focused summarization, which, to a considerable extent, determine the summary quality. Previous work either rarely took into account all above demands simultaneously or dealt with part of them in the dynamic process of choosing sentences to generate a summary. In this paper, we propose a novel approach that integrates all these requirements skillfully by treating them as sentence features, making that the finally generated summary could fully reflect the combinational effect of these properties. Experimental results on the DUC2005 and DUC2006 datasets demonstrate the effectiveness of our approach.