Weighted One Mode Projection of a Bipartite Graph as a Local Similarity Measure

Stram, Rotem; Reuß, Pascal; Althoff, Klaus-Dieter

doi:10.1007/978-3-319-61030-6_26

Cited by 11 publications

(3 citation statements)

References 26 publications

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“…Extensions of this paper could explore methods to deal with this overestimated information and with present biased results. For now, and in an exploratory way, we continue to use a weighted matrix, as if it were binary, for the relationship between each pair of congressmen and the number of bills that they co-sponsored in common (Stram, Reuss, and Althoff 2017). This approach takes into account the different intensity of interaction across pairs of congressmen.…”

Section: Estimating Co-sponsorship Network For the Brazilian House Of...mentioning

confidence: 99%

Co-sponsorship networks in the Brazilian Congress: An exploratory analysis of Caucus influence

Nery

Mueller

2022

Estud. Econ.

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A defining characteristic of the Brazilian Congress is the informal self-organization of legislators from different parties into issue-specific caucuses, known as bancadas. Though not official entities and often loosely delineated, these groups many times play central roles in proposing and approving or blocking legislation. Presidents often negotiate directly with the more influential caucuses instead of going through formal political parties. Caucuses can thus have great influence on public policy, both as a locus of lobbying and rent-seeking or as means of representation and governability. In this paper we use network theory to measure the links between legislators using the co-sponsorship of proposed bills. The analysis identifies and ranks the main caucuses and provides a measure of their influence and power. The networks that emerge from these exercises show that, at least at the level of co-sponsorships, caucuses can provide a better description of the legislators’ behavior and interaction than political parties.

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Section: Estimating Co-sponsorship Network For the Brazilian House Of...mentioning

confidence: 99%

Co-sponsorship networks in the Brazilian Congress: An exploratory analysis of Caucus influence

Nery

Mueller

2022

Estud. Econ.

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“…Word Embeddings 3. Siamese Networks Within any CBR system, building similarity measures are considered one of the most tedious and complicated task [12] [10] [13]. Similarity measures are based on specialised domain knowledge and can identify how close cases are to each other.…”

Section: A Building Similarity Measures Using Deep Learning Models Cmentioning

confidence: 99%

DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NLP Prototyping

Amin

Kapetanakis

Polatidis

et al. 2020

2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA)

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With widespread modernization, digitization and transformations of most of industries, Artificial Intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework-DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture.

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“…O pacote "igraph" (CSARDI; NEPUSZ, 2006) do R na função "bipartite.projection" lida apenas com a segunda questão (parâmetro "multiplicity"), em que o peso entre dois nós projetados é proporcional ao número de nós da outra camada que esses dois nós interagiram em comum (Figura 4.6). Representar os pesos das interações das redes bipartidas na projeção não é um procedimento trivial e até o momento existem alguns algoritmos para realizar o processo, mas sem implementação em R (STRAM; REUSS; ALTHOFF, 2017;ZHOU et al, 2007).…”

Section: Projeção De Redes Bipartidasunclassified

Paisagem e redes de polinização: como manter a polinização na Mata Atlântica?

Rodrigues¹

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Weighted One Mode Projection of a Bipartite Graph as a Local Similarity Measure

Cited by 11 publications

References 26 publications

Co-sponsorship networks in the Brazilian Congress: An exploratory analysis of Caucus influence

Co-sponsorship networks in the Brazilian Congress: An exploratory analysis of Caucus influence

DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NLP Prototyping

Paisagem e redes de polinização: como manter a polinização na Mata Atlântica?

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