This paper describes an analysis of IP-network traffic in terms of the time variation of self-similarity. To get a comprehensive view in analyzing the degree of long-range dependence (LRD) of IP-network traffic, this paper used a self-organizing map, which provides a way to map high-dimensional data onto a low-dimensional domain. Also, in the LRD-based analysis, this paper employed detrended fluctuation analysis (DFA), which is applicable to the analysis of long-range power-law correlations or LRD in non-stationary time-series signals. In applying this method to traffic analysis, this paper performed two kinds of traffic measurement: one based on IP-network traffic flowing into NTT Musashino R&D center (Tokyo, Japan) from the Internet and the other based on IP-network traffic flowing through at an interface point between an access provider (Tokyo, Japan) and the Internet. Based on sequential measurements of IP-network traffic, this paper derived corresponding values for the LRD-related parameter α of measured traffic. As a result, we found that the characteristic of self-similarity seen in the measured traffic fluctuated over time, with different time variation patterns for two measurement locations. In training the self-organizing map, this paper used three parameters: two α values for different plot ranges, and Shannon-based entropy, which reflects the degree of concentration of measured time-series data. We visually confirmed that the traffic data could be projected onto the map in accordance with the traffic properties, resulting in a combined depiction of the effects of the degree of LRD and network utilization rates. The proposed method can deal with multi-dimensional parameters, projecting its results onto a two-dimensional space in which the projected data positions give us an effective depiction of network conditions at different times.