570 MANAGING AND MINING GRAPH DATA from the perspective mining of mining a single (large) network in the presence of noise and uncertainty. Both data mining and the field of bioinformatics are young and vibrant and thus there are ample opportunities for interesting lines of future research at their intersection. Sticking to the theme of this article – graph mining in bioinformatics – below we list several such opportunities. This list is by no means a comprehensive list but highlight some of the potential opportunities researchers may avail of. Scalable algorithms for analyzing time varying networks: A large ma- jority of the work to date in this field has focused on the analysis of static networks. While there have been some recent efforts to analyze dynamic biological networks, research in this arena is at its infancy. With antici- pated advances in technology where much more temporal data is likely to become available temporal analysis of such networks is likely to be an important arena of future research. Underpinning this effort, given the size and dynamics of the data involved are the need to develop scalable algorithms for processing and analyzing such data. Discovering anomalous structures in graph data: Again while most of the work to date has focused on the discovery of frequent or modular structure within such data – the discovery of anomalous substructures often has a crucial role to play in such domains. Defining what con- stitutes an anomaly, how to compute it efficiently while leveraging the ambient knowledge in the domain in question are some of the challenges to be addressed. Integrating data from multiple, possibly conflicting sources: A funda- mental challenge in bioinformatics in general is that of data integration. Data is available in many formats and often times are in conflict. For ex- ample protein interaction data produced by various experimental meth- ods (mass spectrometry, Yeast2Hybrid, in-silico) are often in conflict. Research into methods that are capable of resolving such conflicts while still discovering useful patterns are needed. Incorporating domain information: It has been our observation that often we as data mining researchers tend to under-utilize available domain information. This may arise out of ignorance (the field of bioinformatics is very vast) or simply omitted from the training phase as a means to confirm the utility of the proposed methods (to maintain the sanctity of the validation procedure). We believe a fresh look at how domain knowledge can be embedded in existing approaches and better validation methodologies in close conjunction with domain experts must be looked into. A Survey of Graph Mining Techniques for Biological Datasets 571 Uncertainty-aware and noise-tolerant methods: While this has certainly been an active area of research in the bioinformatics community in gen- eral, and in the field of graph mining in bioinformatics in particular, there are still many open problems here. Incorporating uncertainty is necessarily a domain-dependent issue and probabilistic approaches of- fer exciting possibilities. Additionally leveraging topological, relational and other semantic characteristics of the data effectively is an interesting topic for future research. A related challenge here is to model trust and provenance related information. Ranking and summarizing patterns harvested: While ranking and sum- marizing patterns has been the subject of much research in the data min- ing and network science community the role of such methods in bioin- formatics has been much less researched. 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