PurposeThe growing interest in e‐government raises the question of stages in e‐government development. A few stage models for e‐government have been proposed. Without a common e‐government stage model, different research in e‐government may be based on different stage models. This presents a difficulty in comparing and understanding different research results. In this research, we synthesize the existing e‐government stage models so that there is a common frame of reference for researchers and practitioners in the area.Design/methodology/approachThis research utilizes a qualitative meta‐synthesis methodology to synthesize different e‐government stage models. The meta‐synthesis follows the steps used in meta‐ethnography.FindingsFive different e‐government stage models were used in this research. We translated the stages within different models into one another and developed a new e‐government stage model. The new e‐government stage model has the following five stages: web presence, interaction, transaction, transformation, and e‐democracy.Research limitations/implicationsThe paper contributes to e‐government theory development. The new five‐stage model provides a synthesized conceptual framework for researchers to evaluate and understand e‐government development.Practical implicationsThe synthesized e‐government stage model presents a road map for practitioners to follow in their e‐government projects.Originality/valueThe research uses an innovative and new research methodology to synthesize the existing research. It is one of the first research in the information systems area to make use of meta‐synthesis approach to consolidate the existing qualitative research. This paper is also one of the one papers to systematically come up with an e‐government stage model.

As governments at different levels and all around the world are increasingly using the Web to enhance and improve their services, understanding e-government development and exploring factors that affect e-government development have become important research topics. The purpose of this research is to investigate factors explaining e-government development in terms of social development lenses. Based on growth and regional development theories, the paper hypothesizes that income level, development status, and region are three factors that differentiate egovernment development in countries. Group comparison tests are conducted using secondary data from the United Nations and the United Nations Development Programme. The results support the hypotheses that significant differences in egovernment development exist between countries with respect to the three categorical variables mentioned above. In addition, the paper applies planned post-hoc tests to further investigate the differences. The results of this research are valuable to e-government scholars and practitioners. As the research involves data from more than a hundred countries, the research contributes to understanding e-government development factors on a global scale.

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Graph coloring is a fundamental graph problem that is widely applied in a variety of applications. The aim of graph coloring is to minimize the number of colors used to color the vertices in a graph such that no two incident vertices have the same color. Existing solutions for graph coloring mainly focus on computing a good coloring for a static graph. However, since many real-world graphs are highly dynamic, in this paper, we aim to incrementally maintain the graph coloring when the graph is dynamically updated. We target on two goals: high effectiveness and high efficiency. To achieve high effectiveness, we maintain the graph coloring in a way such that the coloring result is consistent with one of the best static graph coloring algorithms for large graphs. To achieve high efficiency, we investigate efficient incremental algorithms to update the graph coloring by exploring a small number of vertices. We design a color-propagation based algorithm which only explores the vertices within the 2-hop neighbors of the update-related and color-changed vertices. We then propose a novel color index to maintain some summary color information and, thus, bound the explored vertices within the neighbors of these vertices. Moreover, we derive some effective pruning rules to further reduce the number of propagated vertices. The experimental results demonstrate the high effectiveness and efficiency of our approach. PVLDB Reference Format:

Maximal clique enumeration is a fundamental problem in graph theory and has been extensively studied. However, maximal clique enumeration is time-consuming in large graphs and always returns enormous cliques with large overlaps. Motivated by this, in this paper, we study the diversified top-k clique search problem which is to find top-k cliques that can cover most number of nodes in the graph. Diversified top-k clique search can be widely used in a lot of applications including community search, motif discovery, and anomaly detection in large graphs. A naive solution for diversified top-k clique search is to keep all maximal cliques in memory and then find k of them that cover most nodes in the graph by using the approximate greedy max k-cover algorithm. However, such a solution is impractical when the graph is large. In this paper, instead of keeping all maximal cliques in memory, we devise an algorithm to maintain k candidates in the process of maximal clique enumeration. Our algorithm has limited memory footprint and can achieve a guaranteed approximation ratio. We also introduce a novel light-weight PNP-Index, based on which we design an optimal maximal clique maintenance algorithm. We further explore three opti-B Lu Qin mization strategies to avoid enumerating all maximal cliques and thus largely reduce the computational cost. Besides, for the massive input graph, we develop an I/O efficient algorithm to tackle the problem when the input graph cannot fit in main memory. We conduct extensive performance studies on real graphs and synthetic graphs. One of the real graphs contains 1.02 billion edges. The results demonstrate the high efficiency and effectiveness of our approach.