ADAMM: Anomaly Detection of Attributed Multi-graphs with Metadata: A Unified Neural Network Approach

Given a complex graph database of node- and edge-attributed multi-graphs as well as associated metadata for each graph, how can we spot the anomalous instances? Many real-world problems can be cast as graph inference tasks where the graph representation could capture complex relational phenomena (e.g., transactions among financial accounts in a journal entry), along with metadata reflecting tabular features (e.g. approver, effective date, etc.). While numerous anomaly detectors based on Graph Neural Networks (GNNs) have been proposed, none are capable of directly handling directed graphs with multi-edges and self-loops. Furthermore, the simultaneous handling of relational and tabular features remains an unexplored area. In this work we propose ADAMM, a novel graph neural network model that handles directed multi-graphs, providing a unified end-to-end architecture that fuses metadata and graph-level representation learning through an unsupervised anomaly detection objective. Experiments on datasets from two different domains, namely, general-ledger journal entries from different firms (accounting) as well as human GPS trajectories from thousands of individuals (urban mobility) validate ADAMM's generality and detection effectiveness of expert-guided and ground-truth anomalies. Notably, ADAMM outperforms existing baselines that handle the two data modalities (graph and metadata) separately with post hoc synthesis efforts.Comment: Accepted at IEEE BigData 202

Accounting in Partnerships

In 1914, an accounting professor named Arthur Andersen founded a public accounting practise... This paper offers a perspective on partner compensation schemes and the accounting information systems that support them

A review of Nohora García’s “understanding Mattessich and Ijiri: a study of accounting thought”

In her well-researched book, Professor Nohora García offers a refreshing and in-depth look into the glorious past of accounting theorists and their search for answers to above questions posed by the esteemed elders.  The book “Understanding Mattessich and Ijiri: A Study of Accounting Thought” represents the 21st entry of the Studies of Development of Accounting Thought Series, edited by Professor Gary Previts of the Case Western Reserve University and is published by Emerald Publishing, Bingley, UK in the year 2018.Even within the accounting history domain, Professor García’s book takes on an unusual intellectual journey, let alone in the broader accounting scholarly arena. Her work is unusual in at least two dimensions. First, the book’s choice of Mattessich and Ijiri as the two accounting scholars to study poses a formidable challenge because the denseness of these two scholars’ work both in terms of their shared tendencies toward abstraction and their scientific-philosophical orientation. Second, her work singles out one book each from the vast libraries of these two prolific scholars

An Invitation to Theory

In this essay, I wish to invite young scholars to learn, use, and contribute to accounting theory. In this invitation, I argue theory has lineage, is important and can be fun. Its lineage comes from the post-WWII scientific revolution in management education and research. Theory is important because it is the successful interaction between theory and empirical work that ultimately advances an academic discipline. Theory can be fun because when done well, learning, using and contributing to theory can be an enjoyable activity for all scholars, either as consumers or as producers of theory

Informational Feedback Effect, Adverse Selection, and the Optimal Disclosure Policy ∗

Trading in a secondary stock market not only redistributes wealth among investors but also generates information that guides subsequent investment. We provide a positive theory of disclosure that reflects both functions of the secondary stock market. On one hand, disclosure improves firm value by ameliorating adverse selection among investors. On the other hand, disclosure reduces the private incentive to produce information and thus impedes investment effi ciency. This trade-off determines the optimal disclosure policy. Our theory reconciles the disclosure practice with other prominent features of securities regulation and generates new testable predictions. This paper has benefited from feedbacks we have received on a companion paper of ours titled as “Learn from and disclose to the stock market " presented at Pennsylvania State University, University o