Inference of monopartite networks from bipartite systems with different link types

Projections of bipartite or two-mode networks capture co-occurrences, and are used in diverse fields (e.g., ecology, economics, bibliometrics, politics) to represent unipartite networks. A key challenge in analyzing such networks is determining whether an observed number of co-occurrences between two nodes is significant, and therefore whether an edge exists between them. One approach, the fixed degree sequence model (FDSM), evaluates the significance of an edge’s weight by comparison to a null model in which the degree sequences of the original bipartite network are fixed. Although the FDSM is an intuitive null model, it is computationally expensive because it requires Monte Carlo simulation to estimate each edge’s p value, and therefore is impractical for large projections. In this paper, we explore four potential alternatives to FDSM: fixed fill model, fixed row model, fixed column model, and stochastic degree sequence model (SDSM). We compare these models to FDSM in terms of accuracy, speed, statistical power, similarity, and ability to recover known communities. We find that the computationally-fast SDSM offers a statistically conservative but close approximation of the computationally-impractical FDSM under a wide range of conditions, and that it correctly recovers a known community structure even when the signal is weak. Therefore, although each backbone model may have particular applications, we recommend SDSM for extracting the backbone of bipartite projections when FDSM is impractical.

One-mode projecting is extensively used to compress bipartite networks. Since one-mode projection is always less informative than the bipartite representation, a proper weighting method is required to better retain the original information. In this article, inspired by the network-based resource-allocation dynamics, we raise a weighting method which can be directly applied in extracting the hidden information of networks, with remarkably better performance than the widely used global ranking method as well as collaborative filtering. This work not only provides a creditable method for compressing bipartite networks, but also highlights a possible way for the better solution of a long-standing challenge in modern information science: How to do a personal recommendation.

On the degree distribution of projected networks mapped from bipartite networks

Modeling one-mode projection of bipartite networks by tagging vertex information

Bipartite networks that characterize complex relationships among data arise in various domains. The existing bipartite network models are mainly based on a type of relationship between objects, and cannot effectively describe multiple relationships in the real world. In this paper, we propose a multi-relationship bipartite network (MBN) model, which can describe multiple relationships between two types of objects, and realizes simple weighted bipartite network reconstruction. Our model contains three major modules, namely multi-relationship bipartite network modeling (MBNM), multi-relationship aggregation module (MAM) and network reconstruction module (NRM). In MBNM, a multi-relationship bipartite network is proposed to describe multiple relationships between two types of objects. In the MAM, considering that different relationships have different information for the model, we introduce a novel relationship-level attention mechanism, and the aggregation of multiple relationships is carried out through the importance of each relationship. Based on the learning framework, the NRM can learn the potential representations of nodes after multi-relationship aggregation, and design a nonlinear fusion mechanism to reconstruct weighted bipartite network. We conducted extensive experiments on three real-world datasets and the results show that multi-relationship aggregation can effectively improve the performance of the model. In addition, experiments also show that our model can outperform existing competitive baseline method.

Global climate warming has been threatening community biodiversity and ecosystem stability worldwide. Bipartite network is a type of ecological networks defined by interactions between two trophic level species, such as plants and herbivores, where species connections occur exclusively between these groups. However, the mechanisms underlying the effects of increasing temperature on bipartite systems structure and species richness remain poorly understood. Using 156 bipartite networks analyzed under a dynamic modeling framework, we examined the individual and interactive effect of temperature (gradient from 0 to 50°C) and mean body-mass ratio (gradient from - 3 to 6) on the community persistence and connectance. We found that ecosystems biodiversity increased linearly with rising temperature when mean body-mass ratio was low (i.e., resource biomass > consumer biomass). Conversely, under high mean body-mass ratios (i.e., consumer biomass > resource biomass), ecosystems biodiversity exhibited a negative response to temperature elevation. Network connectance (i.e., the proportion of possible feeding links that are realized), however, decreased with increasing temperature at low mean body-mass ratios. At high mean body-mass ratios, community connectance displayed a concave relationship with temperature. Overall, our results demonstrate that the body-mass ratio between consumers and resources serves as a key indicator for predicting the response of biodiversity and structure in antagonistic bipartite networks (specifically, herbivore-plant systems) to global environmental change.

A major mining task for binary matrixes is the extraction of approximate top-k patterns that are able to concisely describe the input data. The top-k pattern discovery problem is commonly stated as an optimization one, where the goal is to minimize a given cost function, e.g., the accuracy of the data description. In this work, we review several greedy algorithms, and discuss PaNDa+, an algorithmic framework able to optimize different cost functions generalized into a unifying formulation. We evaluated the goodness of the algorithm by measuring the quality of the extracted patterns. We adapted standard quality measures to assess the capability of the algorithm to discover both the items and transactions of the patterns embedded in the data. The evaluation was conducted on synthetic data, where patterns were artificially embedded, and on real-world text collection, where each document is labeled with a topic. Finally, in order to qualitatively evaluate the usefulness of the discovered patterns, we exploited PaNDa+ to detect overlapping communities in a bipartite network. The results show that PaNDa+ is able to discover high-quality patterns in both synthetic and real-world datasets.

We describe a class of new algorithms to construct bipartite networks that preserves a prescribed degree and joint-degree (degree-degree) distribution of the nodes. Bipartite networks are graphs that can represent real-world interactions between two disjoint sets, such as actor-movie networks, author-article networks, co-occurrence networks, and heterosexual partnership networks. Often there is a strong correlation between the degree of a node and the degrees of the neighbors of that node that must be preserved when generating a network that reflects the structure of the underling system. Our bipartite 2K (B2K) algorithms generate an ensemble of networks that preserve prescribed degree sequences for the two disjoint set of nodes in the bipartite network, and the joint-degree distribution that is the distribution of the degrees of all neighbors of nodes with the same degree. We illustrate the effectiveness of the algorithms on a romance network using the NetworkX software environment to compare other properties of a target network that are not directly enforced by the B2K algorithms. We observe that when average degree of nodes is low, as is the case for romance and heterosexual partnership networks, then the B2K networks tend to preserve additional properties, such as the cluster coefficients, than algorithms that do not preserve the joint-degree distribution of the original network.

To assess the validity of privacy-preserving synthetic data by comparing results from synthetic versus original EHR data analysis. A published retrospective cohort study on real-world effectiveness of COVID-19 vaccines by Maccabi Healthcare Services in Israel was replicated using synthetic data generated from the same source, and the results were compared between synthetic versus original datasets. The endpoints included COVID-19 infection, symptomatic COVID-19 infection and hospitalization due to infection and were also assessed in several demographic and clinical subgroups. In comparing synthetic versus original data estimates, several metrices were utilized: standardized mean differences (SMD), decision agreement, estimate agreement, confidence interval overlap, and Wald test. Synthetic data were generated five times to assess the stability of results. The distribution of demographic and clinical characteristics demonstrated very small difference (< 0.01 SMD). In the comparison of vaccine effectiveness assessed in relative risk reduction between synthetic versus original data, there was a 100% decision agreement, 100% estimate agreement, and a high level of confidence interval overlap (88.7%-99.7%) in all five replicates across all subgroups. Similar findings were achieved in the assessment of vaccine effectiveness against symptomatic COVID-19 Infection. In the comparison of hazard ratios for COVID 19-related hospitalization and odds ratio for symptomatic COVID-19 Infection, the Wald tests suggested no significant difference between respective effect estimates in all five replicates for all patient subgroups but there were disagreements in estimate and decision metrices in some subgroups and replicates. Overall, comparison of synthetic versus original real-world data demonstrated good validity and reliability. Transparency on the process to generate high fidelity synthetic data and assurances of patient privacy are warranted.

본 연구는 국회의원 구성에 있어서 큰 변화를 갖는 17대 국회의 입법발의 결과를 토대로 국회의원 네트워크 분석을 하였다. 각 개별 국회의원들의 입법활동을 넘어서 전체 입법활동에서 국회의원간 협업관계를 파악하고자 하였다. 17대 국회에서 국회의원들이 제출한 대표발의안 총 5728건을 수집하여 국회의원간 이원모드 네트워크를 프로젝션을 통해 분석하였다. 입법발의안을 매개로 국회의원 간의 네트워크 분석을 결과, 입법과정에서 영향력이 큰 국회의원은 물론 여당과 야당의 입법발의 과정에서의 다른 정당의 국회의원들과의 협업관계의 양상이 다름을 알 수 있다. In study we analyze the network about the legislators in Korean National Assembly. We focused on 17th National Assembly since there were full of important changes in composition. Mutual cooperation is necessary to pass the bills beyond their given mission in legislation. In order to find out the relationship of legislators based on the introducing bills, total 5728 bills in 17th National Assembly, we used bipartite network projection. We can find who is a highly influential legislator and the difference between a ruling party and a main opposition party in aspects of cooperative behavior.

This study examined the spatio-temporal trajectories of the international freight forwarding service (IFFS) in the Yangtze River Delta (YRD) and explored the driving mechanisms of the service. Based on a bipartite network projection from an IFFS firm-city data source, we mapped three IFFS networks in the YRD in 2005, 2010, and 2015. A range of statistical indicators were used to explore changes in the spatial patterns of the three networks. The underlying influence of marketization, globalization, decentralization, and integration was then explored. It was found that the connections between Shanghai and other nodal cities formed the backbones of these networks. The effects of a city’s administrative level and provincial administrative borders were generally obvious. We found several specific spatial patterns associated with IFFS. For example, the four non-administrative centers of Ningbo, Suzhou, Lianyungang, and Nantong were the most connected cities and played the role of gateway cities. Furthermore, remarkable regional equalities were found regarding a city’s IFFS network provision, with notable examples in the weakly connected areas of northern Jiangsu and southwestern Zhejiang. Finally, an analysis of the driving mechanisms demonstrated that IFFS network changes were highly sensitive to the influences of marketization and globalization, while regional integration played a lesser role in driving changes in IFFS networks.

The modeling of genetic networks especially from microarray and related data has become an important aspect of the biosciences. This review takes a fresh look at a specific family of models used for constructing genetic networks, the so-called Boolean networks. The review outlines the various different types of Boolean network developed to date, from the original Random Boolean Network to the current Probabilistic Boolean Network. In addition, some of the different inference methods available to infer these genetic networks are also examined. Where possible, particular attention is paid to input requirements as well as the efficiency, advantages and drawbacks of each method. Though the Boolean network model is one of many models available for network inference today, it is well established and remains a topic of considerable interest in the field of genetic network inference. Hybrids of Boolean networks with other approaches may well be the way forward in inferring the most informative networks.

Named entity recognition (NER) plays a vital role in extracting critical medical entities from health care records, facilitating applications such as clinical decision support and data mining. Developing robust NER models for low-resource languages, such as Estonian, remains a challenge due to the scarcity of annotated data and domain-specific pretrained models. Large language models (LLMs) have proven to be promising in understanding text from any language or domain. This study addresses the development of medical NER models for low-resource languages, specifically Estonian. We propose a novel approach by generating synthetic health care data and using LLMs to annotate them. These synthetic data are then used to train a high-performing NER model, which is applied to real-world medical texts, preserving patient data privacy. Our approach to overcoming the shortage of annotated Estonian health care texts involves a three-step pipeline: (1) synthetic health care data are generated using a locally trained GPT-2 model on Estonian medical records, (2) the synthetic data are annotated with LLMs, specifically GPT-3.5-Turbo and GPT-4, and (3) the annotated synthetic data are then used to fine-tune an NER model, which is later tested on real-world medical data. This paper compares the performance of different prompts; assesses the impact of GPT-3.5-Turbo, GPT-4, and a local LLM; and explores the relationship between the amount of annotated synthetic data and model performance. The proposed methodology demonstrates significant potential in extracting named entities from real-world medical texts. Our top-performing setup achieved an F1-score of 0.69 for drug extraction and 0.38 for procedure extraction. These results indicate a strong performance in recognizing certain entity types while highlighting the complexity of extracting procedures. This paper demonstrates a successful approach to leveraging LLMs for training NER models using synthetic data, effectively preserving patient privacy. By avoiding reliance on human-annotated data, our method shows promise in developing models for low-resource languages, such as Estonian. Future work will focus on refining the synthetic data generation and expanding the method's applicability to other domains and languages.

We propose a novel mechanism to release an accurate and differentially private estimate of the link set of social networks. Differential privacy is one of the most common notations to quantify the privacy level of information publication. Several methods have been proposed to publish edge set information, among which one of the notable mechanisms is based on stratified sampling. While it is very scalable, social network information can be significantly altered by this technique. In fact, when we use mechanism based on stratified sampling, a totally random network may get published even when the input network is sparse. We aim to overcome this drawback in our work. We provide an efficient two-stage mechanism to control the edge set size and quality independently. To confirm the practical utility of our proposal, we apply it to the maximum matching problem when the edge information is spread between two different bipartite networks. We validate through experiments that the error induced by our framework is at least 20 times smaller than that of the original stratified sampling based mechanism when privacy level is 5. In addition, the computation time of our framework is 3 times shorter than the original method.

Network representation learning (NRL) maps vertices into latent vector space for further network inference. The existing algorithms concern more about whether the vectors of two similar nodes be close in latent vector space while the hierarchy proximity has been largely neglected by them. The distribution of the representation vectors needs to reflect the hierarchical structural properties which widely exist in networks. In this paper, we propose a novel network representation learning framework that can encode the interpretable hierarchical structural semantics into the representation vectors. Specifically, we measure the distance and importance degree of nodes in the original network and map the nodes to a tree space. This makes the hierarchical structural relations in the original network be clearly revealed by the tree which is also of good interpretability. In this paper, the local structural proximities and the interpretable hierarchy knowledge are encoded into vector space by optimizing the objective function. Extensive experiments conducted on the realistic data sets demonstrate that the proposed approach outperforms the existing state-of-the-art approaches on tasks of node classification, link prediction, and visualization. Finally, a case study is conducted for further analysis about how the proposed model works.