Iterative Collaborative Filtering for Sparse Matrix Estimation

Matrix estimation or completion has served as a canonical mathematical model for recommendation systems. More recently, it has emerged as a fundamental building block for data analysis as a first step to denoise the observations and predict missing values. Since the dawn of e-commerce, similarity-based collaborative filtering has been used as a heuristic for matrix etimation. At its core, it encodes typical human behavior: you ask your friends to recommend what you may like or dislike. Algorithmically, friends are similar “rows” or “columns” of the underlying matrix. The traditional heuristic for computing similarities between rows has costly requirements on the density of observed entries. In “Iterative Collaborative Filtering for Sparse Matrix Estimation” by Christian Borgs, Jennifer T. Chayes, Devavrat Shah, and Christina Lee Yu, the authors introduce an algorithm that computes similarities in sparse datasets by comparing expanded local neighborhoods in the associated data graph: in effect, you ask friends of your friends to recommend what you may like or dislike. This work provides bounds on the max entry-wise error of their estimate for low rank and approximately low rank matrices, which is stronger than the aggregate mean squared error bounds found in classical works. The algorithm is also interpretable, scalable, and amenable to distributed implementation.

Ujian Kesetiaan Persembahan Dimasa Pandemi Covid-19 Berdasarkan Maleakhi 3:10

The Covid-19 pandemic has disrupted the financial structure of church members. The dilemma when it comes to paying offerings and tithing or making a living. Based on this dilemma gap, the purpose of this study is to describe the offerings and tithes of the Natar district congregation and to analyze the faithfulness of church members in paying tithes and offerings. This research method was qualitative with a descriptive approach. The data collection technique used were financial data from the church treasurer about offerings and tithes giver. The first stage was to create a data graph to see the giving unit and the second stage was analyze the economic situation of the congregation and the level of faithfulness of the congregation. The results of the ongoing COVID-19 pandemic research increase the faith of church members through the provision of integrated offerings that increase during the pandemic and the giving of tithes given every month. Evidence of increased faith is that they are more enthusiastic and active in preaching the gospel.

LinkedImm: a linked data graph database for integrating immunological data

Background Many systems biology studies leverage the integration of multiple data types (across different data sources) to offer a more comprehensive view of the biological system being studied. While SQL (Structured Query Language) databases are popular in the biomedical domain, NoSQL database technologies have been used as a more relationship-based, flexible and scalable method of data integration. Results We have created a graph database integrating data from multiple sources. In addition to using a graph-based query language (Cypher) for data retrieval, we have developed a web-based dashboard that allows users to easily browse and plot data without the need to learn Cypher. We have also implemented a visual graph query interface for users to browse graph data. Finally, we have built a prototype to allow the user to query the graph database in natural language. Conclusion We have demonstrated the feasibility and flexibility of using a graph database for storing and querying immunological data with complex biological relationships. Querying a graph database through such relationships has the potential to discover novel relationships among heterogeneous biological data and metadata.

Supergraph Topology Feature Index for Personalized Interesting Subgraph Query in Large Labeled Graphs

Interesting subgraph query aims to find subgraphs that are isomorphic to the given query graph from a data graph and rank the subgraphs according to their interestingness scores. However, the existing subgraph query approaches are inefficient when dealing with large-scale labeled data graph. This is caused by the following problems: (i) the existing work mainly focuses on unweighted query graphs, while ignoring the impact of query constraints on query results. (ii) Excessive number of subgraph candidates or complex joins between nodes in the subgraph candidates reduce the query efficiency. To solve these problems, this paper proposes an intelligent solution. Firstly, an Isotype Structure Graph Compression (ISGC) strategy is proposed to compress similar nodes in a graph to reduce the size of the graph and avoid unnecessary matching. Then, an auxiliary data structure Supergraph Topology Feature Index (STFIndex) is designed to replace the storage of the original data graph and improve the efficiency of an online query. After that, a partition method based on Edge Label Step Value (ELSV) is proposed to partition the index logically. In addition, a novel Top-K interest subgraph query approach is proposed, which consists of the multidimensional filtering (MDF) strategy, upper bound value (UBV) (Size-c) matching, and the optimizational join (QJ) method to filter out as many false subgraph candidates as possible to achieve fast joins. We conduct experiments on real and synthetic datasets. Experimental results show that the average performance of our approach is 1.35 higher than that of the state-of-the-art approaches when the query graph is unweighted, and the average performance of our approach is 2.88 higher than that of the state-of-the-art approaches when the query graph is weighted.

Event Prediction Based On Large Scale Network Subgraph Convolution

Aiming at the problem of event prediction in large-scale event network, a collapse subgraph convolution (CSGCN) algorithm is proposed, which uses event subgraph to predict the subsequent events of event group. CSGCN algorithm collapses the edge induced event subgraph in large-scale event network, removes the irrelevant event nodes from the subgraph, and forms a new event subgraph. GCN algorithm is used to learn the graph embedding representation of the event subgraph, and the subsequent events of the event group are predicted by comparing the similarity between the graph embedding representation of the event group and the subsequent events. Because only some related nodes are processed each time, the application of the model in large-scale data graph is feasible. Through experiments, we explore and verify the effectiveness of extracting features from subgraphs of large-scale graph by using graph convolution training to obtain graph embedding representation. We find that GCN has better event prediction effect than Euclidean distance and co rotation similarity, which further shows that graph convolution algorithm has good performance in the field of graph feature extraction.

Counterfactual causal analysis on structured data

Data generated in a real-world business environment can be highly connected with intricate relationships among entities. Studying relationships and understanding their dynamics can provide deeper understanding of business events. However, finding important causal relations among entities is a daunting task with heavy dependency on data scientists. Also due to fundamental problem of causal inference it is impossible to directly observe causal effects. Thus, a method is proposed to explain predictive causal relations in an arbitrary linked dataset using counterfactual type causality. The proposed method can generate counterfactual examples with high fidelity in minimal time. It can explain causal relations among any chosen response variable and an arbitrary set of independent causal variables to provide explanations in natural language. The evidence of the explanations is shown in the form of a summarized connected data graph

Counterfactual causal analysis on structured data

Data generated in a real-world business environment can be highly connected with intricate relationships among entities. Studying relationships and understanding their dynamics can provide deeper understanding of business events. However, finding important causal relations among entities is a daunting task with heavy dependency on data scientists. Also due to fundamental problem of causal inference it is impossible to directly observe causal effects. Thus, a method is proposed to explain predictive causal relations in an arbitrary linked dataset using counterfactual type causality. The proposed method can generate counterfactual examples with high fidelity in minimal time. It can explain causal relations among any chosen response variable and an arbitrary set of independent causal variables to provide explanations in natural language. The evidence of the explanations is shown in the form of a summarized connected data graph

Continuous matching of evolving patterns over dynamic graph data

Nowadays, the scale of various graphs soars rapidly, which imposes a serious challenge to develop processing and analytic algorithms. Among them, graph pattern matching is the one of the most primitive tasks that find a wide spectrum of applications, the performance of which is yet often affected by the size and dynamicity of graphs. In order to handle large dynamic graphs, incremental pattern matching is proposed to avoid re-computing matches of patterns over the entire data graph, hence reducing the matching time and improving the overall execution performance. Due to the complexity of the problem, little work has been reported so far to solve the problem, and most of them only solve the graph pattern matching problem under the scenario of the data graph varying alone. In this article, we are devoted to a more complicated but very practical graph pattern matching problem, continuous matching of evolving patterns over dynamic graph data, and the investigation presents a novel algorithm for continuously pattern matching along with changes of both pattern graph and data graph. Specifically, we propose a concise representation of partial matching solutions, which can help to avoid re-computing matches of the pattern and speed up subsequent matching process. In order to enable the updates of data graph and pattern graph, we propose an incremental maintenance strategy, to efficiently maintain the intermediate results. Moreover, we conceive an effective model for estimating step-wise cost of pattern evaluation to drive the matching process. Extensive experiments verify the superiority of .