Main path analysis has been widely used in various fields to detect their development trajectories. However, the previous methods treat every citation equally. In fact, it leaves a question open to scholars considering that there are different citation preferences in different disciplines and at different publication times. There are different citation preferences in different disciplines and at different periods, which are ignored by scholars. In order to deal with the problem in identifying development paths in interdisciplinary research areas, this paper proposes a new main path analysis method. The improved main path analysis considers two factors involved in citation preference, including discipline bias and time bias. An evidence analysis from blockchain domain is conducted to demonstrate the effectiveness of the proposed method. The research result shows that the proposed main path analysis method in this paper can resolve the problem of discipline bias and time bias in interdisciplinary research. Moreover, the improved method provides a more differentiated ranking for citation linkages in the network. Our research can enhance the objectivity of the resulting main paths and promote broader application of the main path analysis.

主路径分析已广泛应用于各个领域，以检测其发展轨迹。但是，以前的方法均会平等对待每个引用。实际上，考虑到不同学科和出版时间的引用偏好不同，这给学者们留下了一个未解决的问题。不同学科，不同时期存在不同的引文偏好，被学者所忽略。为了解决跨学科研究领域发展路径的确定问题，提出了一种新的主​​路径分析方法。改进的主路径分析考虑了两个因素，包括学科偏好和时间偏好。从区块链领域进行了证据分析，以证明该方法的有效性。研究结果表明，本文提出的主路径分析方法可以解决跨学科研究中学科偏差和时间偏差的问题。此外，改进的方法为网络中的引文链接提供了更具差异性的排名。我们的研究可以增强最终主路径的客观性，并促进主路径分析的广泛应用。