Estimating parameters of a multipartite loglinear graph model via the EM algorithm

We will amalgamate the Rash model (for rectangular binary tables) and the newly introduced $\alpha$-$\beta$ models (for random undirected graphs) in the framework of a semiparametric probabilistic graph model. Our purpose is to give a partition of the vertices of an observed graph so that the generated subgraphs and bipartite graphs obey these models, where their strongly connected parameters give multiscale evaluation of the vertices at the same time. In this way, a heterogeneous version of the stochastic block model is built via mixtures of loglinear models and the parameters are estimated with a special EM iteration. In the context of social networks, the clusters can be identified with social groups and the parameters with attitudes of people of one group towards people of the other, which attitudes depend on the cluster memberships. The algorithm is applied to randomly generated and real-word data

The Emerging Scholarly Brain

It is now a commonplace observation that human society is becoming a coherent super-organism, and that the information infrastructure forms its emerging brain. Perhaps, as the underlying technologies are likely to become billions of times more powerful than those we have today, we could say that we are now building the lizard brain for the future organism.Comment: to appear in Future Professional Communication in Astronomy-II (FPCA-II) editors A. Heck and A. Accomazz

Finding and Recommending Scholarly Articles

The rate at which scholarly literature is being produced has been increasing at approximately 3.5 percent per year for decades. This means that during a typical 40 year career the amount of new literature produced each year increases by a factor of four. The methods scholars use to discover relevant literature must change. Just like everybody else involved in information discovery, scholars are confronted with information overload. Two decades ago, this discovery process essentially consisted of paging through abstract books, talking to colleagues and librarians, and browsing journals. A time-consuming process, which could even be longer if material had to be shipped from elsewhere. Now much of this discovery process is mediated by online scholarly information systems. All these systems are relatively new, and all are still changing. They all share a common goal: to provide their users with access to the literature relevant to their specific needs. To achieve this each system responds to actions by the user by displaying articles which the system judges relevant to the user's current needs. Recently search systems which use particularly sophisticated methodologies to recommend a few specific papers to the user have been called "recommender systems". These methods are in line with the current use of the term "recommender system" in computer science. We do not adopt this definition, rather we view systems like these as components in a larger whole, which is presented by the scholarly information systems themselves. In what follows we view the recommender system as an aspect of the entire information system; one which combines the massive memory capacities of the machine with the cognitive abilities of the human user to achieve a human-machine synergy.Comment: 14 pages, part of the forthcoming MIT book "Bibliometrics and Beyond: Metrics-Based Evaluation of Scholarly Research" edited by Blaise Cronin and Cassidy R. Sugimot

Serialized Interacting Mixed Membership Stochastic Block Model

Last years have seen a regain of interest for the use of stochastic block modeling (SBM) in recommender systems. These models are seen as a flexible alternative to tensor decomposition techniques that are able to handle labeled data. Recent works proposed to tackle discrete recommendation problems via SBMs by considering larger contexts as input data and by adding second order interactions between contexts' related elements. In this work, we show that these models are all special cases of a single global framework: the Serialized Interacting Mixed membership Stochastic Block Model (SIMSBM). It allows to model an arbitrarily large context as well as an arbitrarily high order of interactions. We demonstrate that SIMSBM generalizes several recent SBM-based baselines. Besides, we demonstrate that our formulation allows for an increased predictive power on six real-world datasets.Comment: Published at ICDM 202

이종 및 계층 구조 교차 문맥 그래프 합성곱 신경망

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2021. 2. 강유.Given attributed graphs, how can we accurately classify them using both topological structures and node features? Graph classification is a crucial task in data mining, especially in the bioinformatics domain where a chemical compound is represented as a graph of attributed compounds. Although there are existing methods like graph kernels or truncated random walks for graph classification, they do not give good accuracy since they consider features present at a single resolution, i.e., nodes or subgraphs. Such single resolution features result in a biased view of the graph's context, which is nearsighted or too wide, failing to capture comprehensive properties of each graph. In this paper, we propose H₂C₂GCN (Heterogeneous and Hierarchical Cross-context Graph Convolution Network), an accurate end-to-end framework for graph classification. Given multiple input graphs, H₂C₂GCN generates a multi-resolution tree that connects the given graphs by cross-context edges. It gives a unified view of multiple graphs considering both node features and topological structures. We propose a novel hierarchical graph convolutional network to extract the representation of each graph. Extensive experiments on real-world datasets show that H₂C₂GCN provides the state-of-the-art accuracy for graph classification.어떻게 구조적 특성과 노드의 레이블을 활용하여 속성 그래프를 분류 할 수 있을까? 그래프 분류는 데이터 마이닝 분야에서 중대한 과제로 여겨진다, 특히나 생물 정보 영역에서 화학 물질들이 속성 그래프로 표현되어 있는 경우에는 더욱 중요하다. 그러나 기존 연구들은 그래프 커널 방식이나 무작위 행보 방식을 사용하여, 그래프 내에 하나의 해상도 (노드 또는 부분그래프) 에 한정되어서 특징들을 고려한다. 이와 같이 하나의 해상도에 집중하여 특징을 고려할 경우 그래프 전체에 대한 편향된 시선으로 바라볼 수밖에 없다. 즉, 그래프들에 대하여 좁게 또는 넓게 바라보므로 그래프 간의 특징을 구분하는데 큰 어려움이 있다. 이 논문에서는 그래프 분류에 종단 간 학습이 가능한 H₂C₂GCN (Heterogeneous and Hierarchical Cross-context Graph Convolution Network)를 제안한다. 다수의 속성 그래프가 주어졌을 시, H₂C₂GCN는 다수의 해상도를 지닌 교차 문맥 간선이 이어진 트리를 만든다. 이를 통하여 다수의 그래프 간의 노드 레이블 및 구조적 특성의 견해를 담을 수 있다. 만들어진 트리에서 그래프 합성곱 신경망을 사용하여 하여 각 그래프의 임베딩을 추출하게 된다. 실험을 생물 정보 데이터에 대하여 평가를 하여 H₂C₂GCN가 기존 방법들에 비하여 높은 정확도를 가지는 것을 확인할 수 있다.I. Introduction 1 II. Related Works 5 III. Proposed Method 7 3.0.1 Overview 7 3.0.2 Multi-Resolution Mapping 10 3.0.3 Cross-Context Mapping 11 3.0.4 Hierarchical GCN 13 IV. Experiments 15 4.0.1 Experimental Settings 15 4.0.2 Classification Accuracy 19 4.0.3 Model Depth 19 4.0.4 Ablation Study 20 V. Conclusion 22 References 23 Abstract in Korean 25Maste

Multiobjective e-commerce recommendations based on hypergraph ranking

© 2018 Recommender systems are emerging in e-commerce as important promotion tools to assist customers to discover potentially interesting items. Currently, most of these are single-objective and search for items that fit the overall preference of a particular user. In real applications, such as restaurant recommendations, however, users often have multiple objectives such as group preferences and restaurant ambiance. This paper highlights the need for multi-objective recommendations and provides a solution using hypergraph ranking. A general User–Item–Attribute–Context data model is proposed to summarize different information resources and high-order relationships for the construction of a multipartite hypergraph. This study develops an improved balanced hypergraph ranking method to rank different types of objects in hypergraph data. An overall framework is then proposed as a guideline for the implementation of multi-objective recommender systems. Empirical experiments are conducted with the dataset from a review site Yelp.com, and the outcomes demonstrate that the proposed model performs very well for multi-objective recommendations. The experiments also demonstrate that this framework is still compatible for traditional single-objective recommendations and can improve accuracy significantly. In conclusion, the proposed multi-objective recommendation framework is able to handle complex and changing demands for e-commerce customers