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This paper aims to introduce a novel modularity-based framework, Com_Tracker, designed to detect and track community structures in dynamic social networks without recomputing them from scratch at each snapshot. Despite extensive research in this area, existing approaches either require repetitive computations or fail to capture key community behavioral events, both of which limit the ability to generate timely and actionable insights. Efficiently tracking community structures is crucial for real-time decision-making in rapidly evolving networks, while capturing behavioral events is necessary for understanding deeper community dynamics. This study addresses these limitations by proposing a more efficient and adaptive solution. It aims to answer the following questions: How can we efficiently track community structures without recomputation? How can we detect significant community events over time?

Com_Tracker models dynamic social networks as a sequence of snapshots. First, it detects the community structure of the initial snapshot using a static community detection algorithm. Then, for each subsequent time step, Com_Tracker updates the community structure based on the previous snapshot, allowing it to track communities and detect their changes over time. The locus-based adjacency encoding scheme is adopted, and Pearson’s correlation guides the construction of neighboring solutions.

Experiments conducted on various networks demonstrate that Com_Tracker effectively detects community structures and tracks their evolution in dynamic social networks. The results highlight its potential for real-time tracking and provide promising performance outcomes.

Com_Tracker offers valuable insights into community evolution, helping practitioners across fields such as resource management, public security, marketing and public health. By understanding how communities evolve, decision-makers can better allocate resources, enhance targeted strategies and predict future community behaviors, improving overall responsiveness to changes in network dynamics.

Com_Tracker addresses critical gaps in existing research by combining the strengths of modularity maximization with efficient tracking of community changes. Unlike previous methods that either recompute structures or fail to capture behavioral events, Com_Tracker provides an incremental, adaptive framework capable of detecting both community evolution and behavioral changes, enhancing real-world applicability in dynamic environments.

The semantic relations between Arabic word representations were recognized and widely studied in theoretical studies in linguistics many centuries ago. Nonetheless, most of the previous research in automatic information retrieval (IR) focused on stem or root-based indexing, while lemmas and patterns are under-exploited. However, the authors believe that each of the four morphological levels encapsulates a part of the meaning of words. That is, the purpose is to aggregate these levels using more sophisticated approaches to reach the optimal combination which enhances IR.

The authors first compare the state-of-the art Arabic natural language processing (NLP) tools in IR. This allows to select the most accurate tool in each representation level i.e. developing four basic IR systems. Then, the authors compare two rank aggregation approaches which combine the results of these systems. The first approach is based on linear combination, while the second exploits classification-based meta-search.

Combining different word representation levels, consistently and significantly enhances IR results. The proposed classification-based approach outperforms linear combination and all the basic systems.

The work stands by a standard experimental comparative study which assesses several NLP tools and combining approaches on different test collections and IR models. Thus, it may be helpful for future research works to choose the most suitable tools and develop more sophisticated methods for handling the complexity of Arabic language.

The originality of the idea is to consider that the richness of Arabic is an exploitable characteristic and no more a challenging limit. Thus, the authors combine 4 different morphological levels for the first time in Arabic IR. This approach widely overtook previous research results.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2020-0515

A number of approaches and algorithms have been proposed over the years as a basis for automatic indexing. Many of these approaches suffer from precision inefficiency at low recall. The choice of indexing units has a great impact on search system effectiveness. The authors dive beyond simple terms indexing to propose a framework for multi-word terms (MWT) filtering and indexing.

In this paper, the authors rely on ranking MWT to filter them, keeping the most effective ones for the indexing process. The proposed model is based on filtering MWT according to their ability to capture the document topic and distinguish between different documents from the same collection. The authors rely on the hypothesis that the best MWT are those that achieve the greatest association degree. The experiments are carried out with English and French languages data sets.

The results indicate that this approach achieved precision enhancements at low recall, and it performed better than more advanced models based on terms dependencies.

Using and testing different association measures to select MWT that best describe the documents to enhance the precision in the first retrieved documents.

The purpose of this paper is to investigate the impact of firm-specific (i.e. firm size, profitability, leverage, dividend, growth opportunities, management quality and firm age) and country-specific (i.e., gross domestic product [GDP] growth) variables on compensation/remuneration offered to chief executive officers (CEOs) working in different industries of Pakistan.

Panel data techniques, namely, pooled ordinary least squares, fixed effects and random effects methods are used to estimate the results. Moreover, Hausman test is used to choose which estimation method, either fixed effects or random effects, is better to explain the results.

Firm size, profitability, leverage, growth opportunities and age are some important firm-specific factors that have mixed (i.e. positive/negative) impact on CEO compensation in different industries. Variations in results are due to industry dynamics. However, it is important to mention that three key variables, namely, dividend, management quality and GDP growth have shown consistent positive impact on CEO compensation in most of the industries. In sum, results show that firm-specific and country-specific variables have material effects on CEO compensation. Moreover, results are found consistent with the predictions of agency theory and human capital theory.

The authors are sure that findings of this study provide some support to the board of directors to determine the pay slice for CEOs. Moreover, findings provide support to the regulatory authorities in formulating mechanisms to determine the compensation package for CEOs working in different industries and to improve the Code of Corporate Governance.

To the best of the authors’ knowledge, no empirical study in Pakistan has yet estimated the effects of firm-specific and country-specific variables on compensation offered to CEOs working in different industries. Thus, industry-wise analysis provides some new insights to the decision-makers and lays some foundation upon which a more detail analysis could be based.