In this article, we use innovative full-text citation analysis along with supervised topic modeling and network-analysis algorithms to enhance classical bibliometric analysis and publication/author/venue ranking. By utilizing citation contexts extracted from a large number of full-text publications, each citation or publication is represented by a probability distribution over a set of predefined topics, where each topic is labeled by an author-contributed keyword. We then used publication/citation topic distribution to generate a citation graph with vertex prior and edge transitioning probability distributions. The publication importance score for each given topic is calculated by PageRank with edge and vertex prior distributions. To evaluate this work, we sampled 104 topics (labeled with keywords) in review papers. The cited publications of each review paper are assumed to be “important publications” for the target topic (keyword), and we use these cited publications to validate our topic-ranking result and to compare different publication-ranking lists. Evaluation results show that full-text citation and publication content prior topic distribution, along with the classical PageRank algorithm can significantly enhance bibliometric analysis and scientific publication ranking performance, comparing with term frequency–inverted document frequency (tf–idf), language model, BM25, PageRank, and PageRank + language model (p < .001), for academic information retrieval (IR) systems.