Organizational behavior anomaly detection method based on community evolution

Abstract

The invention discloses an organizational behavior anomaly detection method based on community evolution. The organizational behavior anomaly detection method is characterized by comprising the steps of fuzzy community partition based on an EM (Expectation-Maximization) algorithm, community evolution analysis, anomaly subsequence detection and the like. By adopting the organizational behavior anomaly detection method, organizational changes can be described on a medium scale, the sensitivity to the statuses and roles of members in an organization, the changes of an interaction amount and interaction frequency, and an organization evolution direction is very high, possible loss of details due to investigation of the organizational dynamics from the whole organization is avoided; anomalies of different time scales can be obtained by adjusting the length of a subsequence and the number of neighborhood subsequences, the difference between the subsequence and a neighborhood thereof can be amplified by a consistent factor constructed through a reconstruction weight and a reconstruction error, and the resolution and robustness of anomaly detection are enhanced.

Description

Technical field [0001] The invention belongs to the field of organization dynamic analysis, and specifically relates to an organization behavior abnormality detection algorithm based on community evolution, which is suitable for analyzing organization behavior. Background technique [0002] Organization refers to a group composed of closely connected social individuals. The organization is dynamically evolving, and its function depends on the assistance and interaction between the members of the organization. Take social organizations as an example. With the rapid development of information technology and the deepening of globalization trends, social organizations’ internal connections are becoming closer, and inter-organizational reliance is becoming stronger. While bringing convenience and efficiency improvements, it also makes local changes. Once generated, it will produce a wide range of cascading effects. For example, in the economic field, the outbreak of the subprime mort...

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Problems solved by technology

The experiment found that the organizational dynamic description based on the similarity of the organizational network has the following disadvantages: 1) The definition of similarity itself is undirected, so it is not sensitive to the direction of organizational evolution. The similarity curves of the

2) The process of organizational evolution is usually gradual. Quantitative changes gradually accumulate into qualitative changes and evolve into new stages. However, similarity-based methods cannot distinguish such gradual changes, that is, they cannot describe the details of organizational evolution stages.

[0007] The disadvantage of the Shewhart control chart is that it has a low ability to detect small deviations, is sensitive to the normal assumption, and is susceptible to outliers

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